{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "## 下面开始机器学习\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.9.1\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import numpy as np\n",
    "\n",
    "print(tf.__version__)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "数据集来咯"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[5069, 134, 2011, 266, 152, 273, 375, 0, 73, 566, 125, 60, 1313, 33, 1068, 1, 133, 28, 26, 2433, 39, 878, 433, 0, 252, 28, 590, 427, 151, 15, 591, 0, 27, 617, 8, 497, 88, 11, 337, 1], [944, 508, 10, 289, 215, 112, 837, 0, 1560, 22, 1339, 541, 39, 257, 1386, 1, 79, 647, 1750, 1177, 895, 41, 22, 0, 15, 1339, 129, 495, 44, 511, 748, 1]]\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "\n",
    "with open(\"唐诗处理后数据集.pickle\", 'rb') as f:  #打开文件\n",
    "    train_x, test_x, train_y, test_y = pickle.load(f)  #将二进制文件对象转换成 Python 对象\n",
    "print(train_x[:2])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "解读回去"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "['，',\n '。',\n '十',\n '卷',\n '百',\n '一',\n '不',\n '人',\n '三',\n '二',\n '四',\n '五',\n '山',\n '日',\n '六',\n '無',\n '八',\n '七',\n '風',\n '中',\n '上',\n '雲',\n '有',\n '九',\n '春',\n '白',\n '天',\n '何',\n '來',\n '月',\n '花',\n '水',\n '時',\n '相',\n '長',\n '君',\n '歸',\n '秋',\n '年',\n '為',\n '生',\n '自',\n '行',\n '江',\n '見',\n '夜',\n '心',\n '知',\n '李',\n '如',\n '此',\n '得',\n '清',\n '下',\n '高',\n '去',\n '南',\n '空',\n '明',\n '在',\n '子',\n '門',\n '事',\n '客',\n '送',\n '道',\n '未',\n '處',\n '居',\n '東',\n '別',\n '金',\n '歌',\n '王',\n '多',\n '青',\n '是',\n '寒',\n '玉',\n '城',\n '雨',\n '遠',\n '朝',\n '家',\n '落',\n '新',\n '出',\n '今',\n '與',\n '西',\n '應',\n '寄',\n '陽',\n '思',\n '千',\n '前',\n '聲',\n '入',\n '書',\n '路',\n '萬',\n '馬',\n '望',\n '草',\n '我',\n '同',\n '飛',\n '深',\n '樹',\n '和',\n '流',\n '開',\n '將',\n '盡',\n '酒',\n '獨',\n '還',\n '已',\n '聞',\n '回',\n '成',\n '地',\n '煙',\n '光',\n '詩',\n '公',\n '重',\n '可',\n '石',\n '誰',\n '色',\n '林',\n '欲',\n '從',\n '雪',\n '古',\n '作',\n '州',\n '方',\n '向',\n '更',\n '之',\n '：',\n '海',\n '首',\n '樓',\n '看',\n '舊',\n '老',\n '易',\n '張',\n '情',\n '滿',\n '身',\n '然',\n '後',\n '愁',\n '香',\n '名',\n '過',\n '言',\n '黃',\n '衣',\n '能',\n '外',\n '遊',\n '頭',\n '平',\n '起',\n '樂',\n '難',\n '塵',\n '龍',\n '曲',\n '裏',\n '大',\n '安',\n '莫',\n '紅',\n '北',\n '華',\n '松',\n '仙',\n '閑',\n '分',\n '故',\n '複',\n '到',\n '似',\n '初',\n '懷',\n '晚',\n '少',\n '宮',\n '葉',\n '猶',\n '離',\n '當',\n '零',\n '竹',\n '氣',\n '意',\n '題',\n '鳥',\n '柳',\n '贈',\n '因',\n '間',\n '劉',\n '非',\n '文',\n '辭',\n '亦',\n '吟',\n '元',\n '國',\n '邊',\n '暮',\n '所',\n '幾',\n '池',\n '夢',\n '留',\n '漢',\n '孤',\n '餘',\n '逢',\n '問',\n '溪',\n '寺',\n '庭',\n '隱',\n '杜',\n '里',\n '士',\n '—',\n '陰',\n '河',\n '醉',\n '野',\n '隨',\n '物',\n '神',\n '關',\n '世',\n '登',\n '歲',\n '枝',\n '幽',\n '臨',\n '微',\n '泉',\n '坐',\n '露',\n '使',\n '若',\n '終',\n '發',\n '期',\n '小',\n '宿',\n '休',\n '經',\n '鄉',\n '芳',\n '好',\n '霜',\n '台',\n '輕',\n '憶',\n '以',\n '至',\n '早',\n '碧',\n '連',\n '陵',\n '綠',\n '半',\n '波',\n '鶴',\n '太',\n '郎',\n '才',\n '官',\n '亭',\n '先',\n '共',\n '正',\n '對',\n '合',\n '者',\n '近',\n '尋',\n '曾',\n '吹',\n '照',\n '舟',\n '豈',\n '兩',\n '曉',\n '夕',\n '常',\n '影',\n '羅',\n '翠',\n '遙',\n '楚',\n '雙',\n '苦',\n '須',\n '峰',\n '驚',\n '主',\n '園',\n '沙',\n '殘',\n '斷',\n '師',\n '僧',\n '楊',\n '動',\n '靜',\n '悲',\n '木',\n '垂',\n '景',\n '興',\n '卻',\n '川',\n '病',\n '恩',\n '紫',\n '德',\n '吳',\n '奉',\n '章',\n '堂',\n '湖',\n '靈',\n '又',\n '語',\n '絕',\n '久',\n '依',\n '真',\n '鳳',\n '蕭',\n '女',\n '鳴',\n '浮',\n '笑',\n '聽',\n '吾',\n '魚',\n '蒼',\n '皇',\n '忽',\n '通',\n '秦',\n '尚',\n '唯',\n '府',\n '車',\n '丹',\n '甫',\n '星',\n '憐',\n '傳',\n '愛',\n '詠',\n '齊',\n '群',\n '淚',\n '侍',\n '郊',\n '虛',\n '田',\n '帝',\n '游',\n '數',\n '夫',\n '寂',\n '聖',\n '許',\n '昔',\n '會',\n '且',\n '閣',\n '雁',\n '舞',\n '句',\n '只',\n '令',\n '轉',\n '堪',\n '節',\n '洞',\n '散',\n '其',\n '恨',\n '武',\n '軍',\n '往',\n '謝',\n '窮',\n '親',\n '疏',\n '征',\n '蘭',\n '朱',\n '直',\n '胡',\n '說',\n '涼',\n '悠',\n '亂',\n '用',\n '感',\n '始',\n '度',\n '容',\n '桃',\n '火',\n '洛',\n '信',\n '遲',\n '韋',\n '覺',\n '窗',\n '韓',\n '觀',\n '待',\n '賢',\n '足',\n '禦',\n '燕',\n '手',\n '畫',\n '傷',\n '霞',\n '宴',\n '晴',\n '死',\n '喜',\n '勝',\n '學',\n '功',\n '琴',\n '劍',\n '皆',\n '暗',\n '酬',\n '移',\n '顧',\n '即',\n '雖',\n '步',\n '陳',\n '及',\n '帶',\n '識',\n '崔',\n '結',\n '立',\n '珠',\n '塞',\n '岩',\n '顏',\n '羽',\n '逐',\n '周',\n '都',\n '卿',\n '臥',\n '幹',\n '願',\n '歡',\n '徒',\n '岸',\n '食',\n '桂',\n '兒',\n '命',\n '廟',\n '斜',\n '司',\n '隔',\n '友',\n '含',\n '夏',\n '鏡',\n '京',\n '繞',\n '橫',\n '遺',\n '解',\n '殿',\n '湘',\n '棲',\n '孟',\n '倚',\n '盧',\n '禪',\n '己',\n '絲',\n '雜',\n '浪',\n '憂',\n '兵',\n '美',\n '化',\n '玄',\n '舍',\n '於',\n '杯',\n '乘',\n '諸',\n '貴',\n '荒',\n '陸',\n '跡',\n '定',\n '交',\n '薄',\n '疑',\n '爾',\n '徐',\n '本',\n '闕',\n '藥',\n '越',\n '異',\n '飲',\n '商',\n '求',\n '兼',\n '便',\n '惜',\n '拂',\n '爭',\n '騎',\n '臣',\n '鐘',\n '賦',\n '詞',\n '口',\n '忘',\n '錦',\n '翻',\n '勞',\n '燈',\n '頻',\n '兮',\n '端',\n '郡',\n '著',\n '鄭',\n '眼',\n '啼',\n '飄',\n '守',\n '素',\n '念',\n '蒙',\n '第',\n '迎',\n '孫',\n '映',\n '侯',\n '猿',\n '徑',\n '力',\n '榮',\n '音',\n '苔',\n '任',\n '沉',\n '錢',\n '錫',\n '鄰',\n '遇',\n '參',\n '韻',\n '字',\n '史',\n '圖',\n '赴',\n '良',\n '教',\n '梁',\n '惟',\n '極',\n '橋',\n '他',\n '凝',\n '院',\n '船',\n '冷',\n '戰',\n '閒',\n '軒',\n '由',\n '引',\n '禮',\n '弦',\n '永',\n '稀',\n '衰',\n '條',\n '報',\n '折',\n '蓮',\n '悵',\n '搖',\n '住',\n '載',\n '幸',\n '況',\n '罷',\n '目',\n '村',\n '暖',\n '貧',\n '制',\n '細',\n '息',\n '歎',\n '壁',\n '嶺',\n '偏',\n '原',\n '裴',\n '蓬',\n '怨',\n '滄',\n '但',\n '鼓',\n '臺',\n '齋',\n '想',\n '蜀',\n '建',\n '掩',\n '緣',\n '偶',\n '魂',\n '持',\n '內',\n '論',\n '彩',\n '取',\n '漸',\n '接',\n '禹',\n '宜',\n '眠',\n '梅',\n '宅',\n '暫',\n '舉',\n '洲',\n '變',\n '寶',\n '沈',\n '低',\n '鬢',\n '破',\n '紛',\n '佳',\n '戶',\n '訪',\n '宗',\n '釣',\n '面',\n '懸',\n '房',\n '冰',\n '穀',\n '皎',\n '簾',\n '俗',\n '尊',\n '鶯',\n '泛',\n '封',\n '精',\n '館',\n '片',\n '失',\n '晨',\n '骨',\n '傾',\n '島',\n '業',\n '帆',\n '雞',\n '虎',\n '曹',\n '井',\n '獻',\n '浦',\n '攜',\n '弟',\n '代',\n '傍',\n '吏',\n '枕',\n '鴻',\n '龜',\n '秀',\n '既',\n '丘',\n '土',\n '漁',\n '昏',\n '室',\n '迷',\n '盤',\n '賞',\n '賈',\n '唐',\n '理',\n '必',\n '荊',\n '伴',\n '泥',\n '蟬',\n '承',\n '翁',\n '眉',\n '冥',\n '旅',\n '揚',\n '積',\n '詳',\n '渡',\n '根',\n '茲',\n '冠',\n '氏',\n '源',\n '冬',\n '采',\n '盛',\n '陶',\n '再',\n '籍',\n '仍',\n '機',\n '賓',\n '縣',\n '渾',\n '收',\n '管',\n '蘇',\n '敢',\n '雄',\n '浩',\n '鬥',\n '床',\n '眾',\n '志',\n '英',\n '途',\n '階',\n '全',\n '急',\n '薛',\n '狂',\n '溫',\n '牧',\n '嚴',\n '拜',\n '趙',\n '藏',\n '殊',\n '指',\n '稱',\n '那',\n '最',\n '繁',\n '腸',\n '茫',\n '霧',\n '適',\n '殷',\n '種',\n '融',\n '旗',\n '賀',\n '戎',\n '廣',\n '性',\n '席',\n '竟',\n '比',\n '衡',\n '每',\n '遂',\n '嘉',\n '形',\n '涯',\n '象',\n '閉',\n '哀',\n '烏',\n '修',\n '侵',\n '旌',\n '乃',\n '慚',\n '荷',\n '昭',\n '寥',\n '維',\n '澤',\n '昨',\n '于',\n '列',\n '牛',\n '尺',\n '燭',\n '髮',\n '恐',\n '銀',\n '各',\n '攀',\n '次',\n '津',\n '戴',\n '抱',\n '憑',\n '答',\n '省',\n '被',\n '姚',\n '苑',\n '危',\n '淒',\n '投',\n '桑',\n '筆',\n '莊',\n '曙',\n '郭',\n '惆',\n '調',\n '放',\n '哭',\n '把',\n '集',\n '寧',\n '宵',\n '角',\n '童',\n '貫',\n '喧',\n '屋',\n '降',\n '負',\n '法',\n '計',\n '營',\n '甘',\n '銷',\n '遍',\n '淩',\n '塘',\n '禁',\n '叢',\n '岑',\n '嗟',\n '消',\n '並',\n '夷',\n '縱',\n '濕',\n '谷',\n '輪',\n '昌',\n '菊',\n '駕',\n '招',\n '篇',\n '瑤',\n '驛',\n '毛',\n '沒',\n '淮',\n '潭',\n '廬',\n '鸞',\n '短',\n '兄',\n '嶽',\n '誠',\n '走',\n '幕',\n '漏',\n '斯',\n '點',\n '淺',\n '而',\n '員',\n '勢',\n '宣',\n '程',\n '潮',\n '仁',\n '赤',\n '催',\n '茅',\n '奇',\n '盈',\n '勤',\n '輿',\n '晝',\n '蓋',\n '筵',\n '戲',\n '繡',\n '巴',\n '達',\n '陪',\n '枯',\n '豔',\n '弄',\n '澗',\n '愈',\n '霄',\n '雷',\n '綺',\n '渚',\n '粉',\n '禽',\n '陌',\n '漫',\n '慶',\n '沾',\n '存',\n '瓊',\n '驅',\n '巢',\n '掃',\n '強',\n '肯',\n '淨',\n '妾',\n '妝',\n '峽',\n '叔',\n '俱',\n '籠',\n '披',\n '義',\n '潛',\n '棹',\n '丞',\n '利',\n '壯',\n '益',\n '燒',\n '延',\n '升',\n '避',\n '疾',\n '輝',\n '迢',\n '佩',\n '響',\n '羨',\n '宋',\n '試',\n '爐',\n '遣',\n '銜',\n '□',\n '稹',\n '桐',\n '呈',\n '借',\n '掛',\n '壽',\n '襟',\n '背',\n '奏',\n '權',\n '詔',\n '擬',\n '境',\n '滴',\n '底',\n '也',\n '服',\n '滅',\n '唱',\n '聊',\n '圓',\n '除',\n '威',\n ...]"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import json\n",
    "\n",
    "with open('中文到整数转换表.json') as f:\n",
    "    j = json.load(f)\n",
    "    # print(j)\n",
    "    整数到中文转换表 = ['' for i in range(len(j))]\n",
    "    for (汉字, 数字) in j.items():\n",
    "        整数到中文转换表[数字] = 汉字\n",
    "整数到中文转换表"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "'浮雲金絡膝，昨日別朱輪。銜草如懷戀，嘶風尚意頻。曾將比君子，不是換佳人。從此西歸路，應容躡後塵。'"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# https://blog.csdn.net/gavin_john/article/details/50717695 中括号取item运算符重载\n",
    "def decode_poetry(num_array):\n",
    "    return \"\".join(map(整数到中文转换表.__getitem__, num_array))\n",
    "\n",
    "\n",
    "a = train_x[2200]\n",
    "decode_poetry(a)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "['白居易',\n '杜甫',\n '李白',\n '齊己',\n '劉禹錫',\n '元稹',\n '李商隱',\n '貫休',\n '韋應物',\n '陸龜蒙',\n '劉長卿',\n '許渾',\n '皎然',\n '杜牧',\n '羅隱',\n '張籍',\n '姚合',\n '錢起',\n '賈島',\n '孟郊',\n '王建',\n '岑參',\n '韓愈',\n '張祜',\n '皮日休',\n '王維',\n '溫庭筠',\n '權德輿',\n '方幹',\n '韋莊',\n '杜荀鶴',\n '盧綸',\n '韓偓',\n '張說',\n '戴叔倫',\n '李中',\n '吳融',\n '李端',\n '薛能',\n '孟浩然',\n '趙嘏',\n '徐鉉',\n '徐夤',\n '皇甫冉',\n '李群玉',\n '李賀',\n '顧況',\n '司空圖',\n '高適',\n '李嶠',\n '李頻',\n '鄭穀',\n '黃滔',\n '施肩吾',\n '周曇',\n '張九齡',\n '宋之問',\n '武元衡',\n '鮑溶',\n '耿湋',\n '李益',\n '儲光羲',\n '司空曙',\n '沈佺期',\n '王昌齡',\n '柳宗元',\n '馬戴',\n '朱慶餘',\n '張喬',\n '李洞',\n '唐彥謙',\n '韓翃',\n '胡曾',\n '楊巨源',\n '曹松',\n '羅鄴',\n '李鹹用',\n '劉得仁',\n '許棠',\n '李德裕',\n '李嘉佑',\n '李頎',\n '李紳',\n '駱賓王',\n '雍陶',\n '戎昱',\n '鄭谷',\n '劉商',\n '陳陶',\n '盧照鄰',\n '李涉',\n '呂岩',\n '蘇頲',\n '曹鄴',\n '呂溫',\n '劉滄',\n '張蠙',\n '崔塗',\n '項斯',\n '無可',\n '羊士諤',\n '周賀',\n '盧仝',\n '薛逢',\n '徐凝',\n '元結',\n '陳子昂',\n '李世民',\n '李建勳',\n '獨孤及',\n '王勃',\n '歐陽詹',\n '殷堯藩',\n '李山甫',\n '薛濤',\n '吳筠',\n '孫元晏',\n '劉駕',\n '劉兼',\n '郎士元',\n '顧非熊',\n '劉言史',\n '李郢',\n '嚴維',\n '章孝標',\n '崔道融',\n '王貞白',\n '李隆基',\n '喻鳧',\n '陳羽',\n '牟融',\n '楊衡',\n '孫逖',\n '汪遵',\n '裴夷直',\n '王周',\n '崔國輔',\n '常建',\n '令狐楚',\n '裴說',\n '於鵠',\n '于武陵',\n '段成式',\n '皇甫曾',\n '張繼',\n '高駢',\n '魚玄機',\n '武則天',\n '包佶',\n '崔顥',\n '崔峒',\n '王績',\n '李乂',\n '聶夷中',\n '賈至',\n '靈一',\n '杜審言',\n '曹唐',\n '張謂',\n '竇鞏',\n '譚用之',\n '楊凝',\n '儲嗣宗',\n '司馬紮',\n '李煜',\n '於濆',\n '秦韜玉',\n '虞世南',\n '秦系',\n '徐彥伯',\n '祖詠',\n '魏征',\n '孫光憲',\n '陸暢',\n '李遠',\n '李咸用',\n '唐求',\n '褚亮',\n '李昌符',\n '崔湜',\n '劉希夷',\n '高蟾',\n '楊炯',\n '姚鵠',\n '邵謁',\n '林寬',\n '尚顏',\n '李適',\n '王涯',\n '馮延巳',\n '張泌',\n '雍裕之',\n '翁承贊',\n '孟貫',\n '張仲素',\n '顏真卿',\n '熊孺登',\n '崔櫓',\n '裴度',\n '劉方平',\n '李百藥',\n '裴迪',\n '鄭巢',\n '盧肇',\n '來鵠',\n '蘇拯',\n '杜光庭',\n '許敬宗',\n '殷文圭',\n '劉憲',\n '朱放',\n '張南史',\n '劉叉',\n '劉威',\n '修睦',\n '歐陽炯',\n '盧象',\n '綦毋潛',\n '竇常',\n '沈亞之',\n '章碣',\n '鄭愔',\n '子蘭',\n '韓琮',\n '和凝',\n '周樸',\n '竇群',\n '朱灣',\n '於鄴',\n '喬知之',\n '長孫佐輔',\n '孫魴',\n '趙彥昭',\n '周繇',\n '伍喬',\n '清江',\n '毛文錫',\n '楊師道',\n '孟雲卿',\n '賀知章',\n '楊淩',\n '竇牟',\n '周朴',\n '陸希聲',\n '上官儀',\n '竇庠',\n '許彬',\n '成彥雄',\n '董思恭',\n '皇甫松',\n '蕭穎士',\n '包何',\n '楊憑',\n '李九齡',\n '胡宿',\n '李冶',\n '沈彬',\n '陶翰',\n '張子容',\n '郭震',\n '于鵠',\n '孟遲',\n '喻坦之',\n '任翻',\n '靈澈',\n '劉昚虛',\n '王翰',\n '王轂',\n '李珣',\n '崔融1',\n '趙冬曦',\n '張又新',\n '王初',\n '顧夐',\n '法振',\n '徐氏',\n '武平一',\n '梁鍠',\n '劉複',\n '朱景玄',\n '張賁',\n '廣宣',\n '棲白',\n '翁綬',\n '李華',\n '閻朝隱',\n '牛嶠',\n '蘇味道',\n '崔曙',\n '暢當',\n '張碧',\n '鄭畋',\n '褚載',\n '王仁裕',\n '劉昭禹',\n '蔣吉',\n '鄭遨',\n '虛中',\n '薛稷',\n '盧延讓',\n '崔日用',\n '莊南傑',\n '陳子良',\n '盧僎',\n '丘為',\n '李紓',\n '薛據',\n '王季友1',\n '丘丹',\n '盧殷',\n '楊發',\n '翁洮',\n '毛熙震',\n '丁仙芝',\n '馬懷素',\n '張諤',\n '徐安貞',\n '柳中庸',\n '薛存誠',\n '韋處厚',\n '陳去疾',\n '蔣防',\n '陳標',\n '薛瑩',\n '吳仁璧',\n '孔德紹',\n '楊夔',\n '護國',\n '棲蟾',\n '尹鶚',\n '徐堅',\n '于濆',\n '李廓',\n '鄭錫',\n '姚系',\n '王灣',\n '冷朝陽',\n '崔玨',\n '于鄴',\n '熊皎',\n '左偃',\n '水神',\n '袁暉',\n '王睿',\n '韋元旦',\n '盧鴻一',\n '常袞',\n '竇叔向',\n '李赤',\n '費冠卿',\n '韋蟾',\n '蔣貽恭',\n '可止',\n '李逢吉',\n '李約',\n '王縉',\n '萬楚',\n '李義府',\n '陳叔達',\n '武三思',\n '蕭至忠',\n '楊汝士',\n '崔涯',\n '歐陽袞',\n '李昭象',\n '盧士衡',\n '江為',\n '吳商浩',\n '處默',\n '李治',\n '姚崇',\n '孟簡',\n '韋承慶',\n '郎大家宋氏',\n '張文琮',\n '吳少微',\n '薛曜',\n '盧藏用',\n '包融',\n '陳潤',\n '鮑防',\n '張登',\n '李敬方',\n '顧雲',\n '孟賓於',\n '韓溉',\n '伊用昌',\n '薛昭蘊',\n '魏承班',\n '李顯',\n '李忱',\n '上官昭容',\n '鄭絪',\n '張袞',\n '張循之',\n '王無競',\n '王適',\n '厲玄',\n '薛奇童',\n '王諲',\n '張潮',\n '崔液',\n '張志和',\n '劉孝孫',\n '孔紹安',\n '宗楚客',\n '于季子',\n '韋嗣立',\n '賀朝',\n '蔣冽',\n '朱長文',\n '于良史',\n '陳翊',\n '崔元翰',\n '麹信陵',\n '李翱',\n '白行簡',\n '舒元輿',\n '霍總',\n '王駕',\n '錢珝',\n '劉象',\n '徐仲雅',\n '廖融',\n '史鳳',\n '閻選',\n '李昂1',\n '徐賢妃',\n '李衍',\n '賈曾',\n '牛僧孺',\n '李舒',\n '鄭世翼',\n '王之渙',\n '李暇',\n '張柬之',\n '李康成',\n '張彪',\n '田娥',\n '任希古',\n '宋璟',\n '張均',\n '岑羲',\n '胡皓',\n '張旭',\n '劉灣',\n '沈頌',\n '閻防',\n '嚴武',\n '章八元',\n '陳存',\n '崔護',\n '王起',\n '林滋',\n '李宣古',\n '鄭損',\n '李沇',\n '鄭准',\n '王岩',\n '馮道',\n '陳貺',\n '牛希濟',\n '詹敦仁',\n '王元',\n '張夫人',\n '張窈窕',\n '義淨',\n '歸仁',\n '可朋',\n '慕幽',\n '許堅2',\n '蜀宮群仙',\n '李貞白',\n '李璟',\n '段文昌',\n '張易之',\n '郭元振',\n '袁朗',\n '李嶷',\n '陸長源',\n '庾抱',\n '劉禕之',\n '徐晶',\n '李泌',\n '許景先',\n '蔡希寂',\n '殷遙',\n '王泠然',\n '崔興宗',\n '徐九皋',\n '閻寬',\n '于邵',\n '沈千運',\n '呂渭',\n '崔備',\n '徐敞',\n '張聿',\n '李正封',\n '鄭澣',\n '李程',\n '楊嗣複',\n '沈傳師',\n '周匡物',\n '袁不約',\n '楊乘',\n '趙璜',\n '潘鹹',\n '鄭綮',\n '歐陽玭',\n '公乘億',\n '盧汝弼',\n '楊凝式',\n '黃損',\n '韓熙載',\n '潘佑',\n '廖匡圖',\n '徐鍇',\n '許堅1',\n '湯悅',\n '吳越人',\n '李濤',\n '盧休',\n '李範',\n '晁采',\n '姚月華',\n '梁瓊',\n '趙鸞鸞',\n '慧淨',\n '隱巒',\n '雲台峰女仙',\n '陳季卿',\n '權龍褒',\n '文丙',\n '鮑君徽',\n '韓休',\n '郭子儀',\n '李回',\n '員半千',\n '許孟容',\n '崔邠',\n '趙光逢',\n '紀唐夫',\n '王偃',\n '辛弘智',\n '張紘',\n '韋渠牟',\n '岑文本',\n '陸敬',\n '楊浚',\n '劉允濟',\n '韓仲宣',\n '高瑾',\n '崔泰之',\n '魏知古',\n '王琚',\n '李迥秀',\n '趙彥伯',\n '源幹曜',\n '裴漼',\n '韋述',\n '劉庭琦',\n '張嘉貞',\n '席豫',\n '沈如筠',\n '李邕',\n '萬齊融',\n '蔣維翰',\n '孫昌胤',\n '張鼎',\n '馮著',\n '蔣渙',\n '元季川',\n '陸贄',\n '王烈',\n '奚賈',\n '謝良輔',\n '劉迥',\n '皇甫澈',\n '李吉甫',\n '李觀',\n '李絳',\n '姚康',\n '馬異',\n '裴次元',\n '王魯複',\n '李渤',\n '柳公權',\n '張蕭遠',\n '何希堯',\n '柳棠',\n '祝元膺',\n '王鐸',\n '李玖',\n '莫宣卿',\n '鄭愚',\n '袁郊',\n '蕭遘',\n '袁皓',\n '鄭仁表',\n '鄭璧',\n '溫憲',\n '孫偓',\n '路德延',\n '胡令能',\n '孫棨',\n '張為',\n '劉斌',\n '羅紹威',\n '宋齊丘',\n '廖凝',\n '李家明',\n '翁宏',\n '劉乙',\n '胡玢',\n '狄煥',\n '楊希道',\n '鄭鏦',\n '紇幹著',\n '周濆',\n '馬逢',\n '吉師老',\n '姚揆',\n '易思',\n '賈彥璋',\n '韓常侍',\n '陳甫',\n '卞震',\n '趙氏2',\n '薛馧',\n '張文姬',\n '步非煙',\n '孟氏',\n '崔萱',\n '崔仲容',\n '慧宣',\n '善生',\n '卿雲',\n '馬湘',\n '張辭',\n '沈廷瑞',\n '卓英英',\n '張元一',\n '李存勖',\n '鹿虔扆',\n '李亨',\n '錢鏐',\n '盧從願',\n '蔡孚',\n '盧懷慎',\n '劉晏',\n '鄭餘慶',\n '蕭仿',\n '馮伉',\n '賈馳',\n '鄭渥',\n '東方虯',\n '朱光弼',\n '杜頠',\n '齊浣',\n '張若虛',\n '李希仲',\n '賀蘭進明',\n '常理',\n '李景伯',\n '盧貞',\n '謝偃',\n '長孫無忌',\n '杜淹',\n '崔善為',\n '歐陽詢',\n '元萬頃',\n '陳元光',\n '崔知賢',\n '陳嘉言',\n '張敬忠',\n '張昌宗',\n '喬備',\n '尹懋',\n '李崇嗣',\n '韋安石',\n '李元紘',\n '王丘',\n '周瑀',\n '孫處玄',\n '徐延壽',\n '李憕',\n '李昂2',\n '李林甫',\n '陳希烈',\n '宋昱',\n '崔翹',\n '陸海',\n '沈宇',\n '張萬頃',\n '樓穎',\n '劉太真',\n '褚朝陽',\n '畢耀',\n '趙征明',\n '任華',\n '韓滉',\n '韋夏卿',\n '張眾甫',\n '丁澤',\n '王表',\n '何兆',\n '陸羽',\n '鄭常',\n '竇參',\n '韋皋',\n '崔子向',\n '柳公綽',\n '林藻',\n '張薦',\n '潘孟陽',\n '崔立之',\n '範傳正',\n '張賈',\n '張文規',\n '張匯',\n '陳通方',\n '皇甫湜',\n '盧拱',\n '劉猛',\n '葉季良',\n '湛賁',\n '周弘亮',\n '張仲方',\n '崔玄亮',\n '符載',\n '孫叔向',\n '劉皂',\n '林傑',\n '蔡京',\n '楊敬之',\n '陳至',\n '鄭還古',\n '朱晝',\n '滕邁',\n '李餘',\n '白敏中',\n '常楚老',\n '平曾',\n '魏扶',\n '楊收',\n '鄭史',\n '元晦',\n '黃頗',\n '劉綺莊',\n '楊牢',\n '潘緯',\n '武瓘',\n '李騭',\n '張孜',\n '趙鴻',\n '李縠',\n '顏萱',\n '顧在鎔',\n '王渙',\n '戴司顏',\n '孫合',\n '李琪',\n '盧頻',\n '鄭良士',\n '伍唐珪',\n '陳光',\n '捧劍僕',\n '黃巢',\n '羅袞',\n '鐘謨',\n '王感化',\n '馮涓',\n '楊玢',\n '詹琲',\n '張立',\n '蘇廣文',\n '尉遲匡',\n '繆島雲',\n '夏寶松',\n '庸仁傑',\n '李堯夫',\n '段義宗',\n '李舜弦',\n '王韞秀',\n '孫氏',\n '程長文',\n '崔鶯鶯',\n '劉雲',\n '張琰',\n '劉媛',\n '劉瑤',\n '廉氏',\n '關盼盼',\n '王福娘',\n '徐月英',\n '元淳',\n '寒山',\n '景雲',\n '法照',\n '知玄',\n '澹交',\n '若虛',\n '曇域',\n '幹康',\n '惟審',\n '許宣平',\n '李夢符',\n '張白',\n '眉娘',\n '上元夫人',\n '滕傳胤',\n '湘中蛟女',\n '龍女',\n '何光遠',\n '韋璜',\n '西施',\n '王軒',\n '劉行敏',\n '裴諝',\n '徐昌圖',\n '李旦',\n '李賢',\n '孟昶',\n '韓思複',\n '劉晃',\n '王晙',\n '崔玄童',\n '何鸞',\n '蔣挺',\n '源光裕',\n '姜皎',\n '薑晞',\n '夏侯孜',\n '張齊賢',\n '鄭善玉',\n '胡雄',\n '祝欽明',\n '陳京',\n '歸登',\n '杜羔',\n '張昭',\n '劉氏雲',\n '竇威',\n '歐陽瑾',\n '趙微明',\n '梁獻',\n '顧朝陽',\n '梁氏瓊',\n '吳燭',\n '張修之',\n '裴交泰',\n '嚴識玄',\n '張烜',\n '王沈',\n '柯崇',\n '鄒紹先',\n '虞羽客',\n '張熾',\n '王訓',\n '李章',\n '滕潛',\n '王珪',\n '杜正倫',\n '崔信明',\n '馬周',\n '張文恭',\n '李敬玄',\n '楊思玄',\n '杜易簡',\n '趙謙光',\n '張鷟',\n '魏元忠',\n '李懷遠',\n '蘇瑰',\n '高正臣',\n '高球',\n '弓嗣初',\n '長孫正隱',\n '周彥暉',\n '高嶠',\n '周思鈞',\n '崔日知',\n '楊廉',\n '張錫',\n '解琬',\n '蕭嵩',\n '陸堅',\n '李適之',\n '鄭繇',\n '蘇晉',\n '王光庭',\n '裴耀卿',\n '宋鼎',\n '張宣明',\n '蔡隱丘',\n '張翬',\n '談戭',\n '樊晃',\n '邢巨',\n '薛業',\n '袁瓘',\n '寇坦',\n '李休烈',\n '楊炎',\n '範朝',\n '張巡',\n '韋丹',\n '蕭昕',\n '楊諫',\n '趙良器',\n '郭良',\n '李收',\n '屈同仙',\n '豆盧複',\n '芮挺章',\n '陳季',\n '王邕',\n '李棲筠',\n '徐浩',\n '薛令之',\n '袁傪',\n '崔何',\n '王緯',\n '郭澹',\n '令狐峘',\n '蘇源明',\n '蘇渙',\n '韋建',\n '殷寅',\n '李岑2',\n '韋迢',\n '張濯',\n '姚倫',\n '張叔卿',\n '鄭丹',\n '張建封',\n '崔膺',\n '馮宿',\n '王武陵',\n '張佐',\n '閻濟美',\n '張少博',\n '周渭',\n '周存',\n '黎逢',\n '苗發',\n '衛象',\n '柳郴',\n '鄭概',\n '範燈',\n '樊珣',\n '劉蕃',\n '張松齡',\n '劉長川',\n '鄭審',\n '李幼卿',\n '羅讓',\n '李願',\n '蕭祜',\n '王良士',\n '顏粲',\n '張正元',\n '彭伉',\n '崔樞',\n '張嗣初',\n '許康佐',\n '楊于陵',\n '武少儀',\n '姚向',\n '溫會',\n '李敬伯',\n '郭遵',\n '許稷',\n '胡證',\n '席夔',\n '盧儲',\n '周元範',\n '王炎',\n '陳昌言',\n '李宣遠',\n '陳翥',\n '王播',\n '宋濟',\n '蘇郁',\n '蘇鬱',\n '於頔',\n '吳武陵',\n '封敖',\n '楊虞卿',\n '趙蕃',\n '唐扶',\n '侯冽',\n '李播',\n '滕倪',\n '劉虛白',\n '郭良驥',\n '柳泌',\n '朱沖和',\n '張光朝',\n '裴潾',\n ...]"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import json\n",
    "\n",
    "with open('作者到整数转换表.json') as f:\n",
    "    j = json.load(f)\n",
    "    # print(j)\n",
    "    整数到作者转换表 = ['' for i in range(len(j))]\n",
    "    for (作者, 数字) in j.items():\n",
    "        整数到作者转换表[数字] = 作者\n",
    "整数到作者转换表"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "int32\n"
     ]
    },
    {
     "data": {
      "text/plain": "'劉禹錫'"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def decode_poet(num):\n",
    "    return 整数到作者转换表[num]\n",
    "\n",
    "\n",
    "a = train_y[2200]\n",
    "print(a.dtype)\n",
    "# print(a)\n",
    "# a[0]\n",
    "decode_poet(a)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "整数数组必须在输入神经网络之前转换为张量。这种转换可以通过以下两种方式来完成：\n",
    "将数组转换为表示单词出现与否的由 0 和 1 组成的向量，类似于 one-hot 编码。例如，序列[3, 5]将转换为一个 10,000 维的向量，该向量除了索引为 3 和 5 的位置是 1 以外，其他都为 0。然后，将其作为网络的首层——一个可以处理浮点型向量数据的稠密层。不过，这种方法需要大量的内存，需要一个大小为 num_words * num_reviews 的矩阵。\n",
    "或者，我们可以填充数组来保证输入数据具有相同的长度，然后创建一个大小为 max_length * num_reviews 的整型张量。我们可以使用能够处理此形状数据的嵌入层作为网络中的第一层。\n",
    "在本教程中，我们将使用第二种方法。\n",
    "由于电影评论长度必须相同，我们将使用 pad_sequences 函数来使长度标准化："
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "64\n",
      "64\n"
     ]
    }
   ],
   "source": [
    "print(max(map(len, train_x)))\n",
    "print(max(map(len, test_x)))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "                  0\ncount  14897.000000\nmean      41.118547\nstd        8.677882\nmin       12.000000\n25%       32.000000\n50%       40.000000\n75%       48.000000\nmax       64.000000",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>14897.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>41.118547</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>8.677882</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>12.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>32.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>40.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>48.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>64.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l = list(map(len, train_x))\n",
    "import pandas as pd\n",
    "\n",
    "l_df = pd.DataFrame(l)\n",
    "l_df.describe()  # 诗歌有多长"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [],
   "source": [
    "七言律诗长度 = 7 * 8 + 1 * 8"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [],
   "source": [
    "vocab_size = 7611\n",
    "train_data = keras.preprocessing.sequence.pad_sequences(train_x,\n",
    "                                                        value=vocab_size,\n",
    "                                                        padding='post',\n",
    "                                                        maxlen=七言律诗长度)\n",
    "\n",
    "test_data = keras.preprocessing.sequence.pad_sequences(test_x,\n",
    "                                                       value=vocab_size,\n",
    "                                                       padding='post',\n",
    "                                                       maxlen=七言律诗长度)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "癡頑終日羨人閑，卻喜因官得近山。斜對寺樓分寂寂，文案把來看未會，雖書一字甚慚顏。\n",
      "王建\n"
     ]
    },
    {
     "data": {
      "text/plain": "(64, 64)"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(decode_poetry([i for i in test_data[0] if i != vocab_size]))\n",
    "print(decode_poet(test_y[0]))\n",
    "len(train_data[0]), len(train_data[1])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 49, 50, 51, 52, 53, 54, 57, 59, 62, 67, 68, 72}\n",
      "{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 49, 50, 51, 52, 53, 54, 57, 59, 62, 67, 68, 72}\n",
      "60\n"
     ]
    },
    {
     "data": {
      "text/plain": "60"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(set(train_y))\n",
    "print(set(test_y))\n",
    "print(max(len(set(train_y)), len(set(test_y))))\n",
    "len(set(train_y))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "data": {
      "text/plain": "{0: 0,\n 1: 1,\n 2: 2,\n 3: 3,\n 4: 4,\n 5: 5,\n 6: 6,\n 7: 7,\n 8: 8,\n 9: 9,\n 10: 10,\n 11: 11,\n 12: 12,\n 13: 13,\n 14: 14,\n 15: 15,\n 16: 16,\n 17: 17,\n 18: 18,\n 19: 19,\n 20: 20,\n 21: 21,\n 22: 22,\n 23: 23,\n 24: 24,\n 25: 25,\n 26: 26,\n 27: 27,\n 28: 28,\n 29: 29,\n 30: 30,\n 31: 31,\n 32: 32,\n 33: 33,\n 34: 34,\n 35: 35,\n 36: 36,\n 37: 37,\n 38: 38,\n 39: 39,\n 40: 40,\n 41: 41,\n 42: 42,\n 43: 43,\n 44: 44,\n 45: 45,\n 46: 46,\n 47: 47,\n 49: 48,\n 50: 49,\n 51: 50,\n 52: 51,\n 53: 52,\n 54: 53,\n 57: 54,\n 59: 55,\n 62: 56,\n 67: 57,\n 68: 58,\n 72: 59}"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_author2old_author = list(set(train_y))\n",
    "new_author2old_author\n",
    "old_author2new_author = dict()\n",
    "for i in range(len(new_author2old_author)):\n",
    "    old_author2new_author[new_author2old_author[i]] = i\n",
    "old_author2new_author"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "data": {
      "text/plain": "array([20, 32, 31, ..., 16, 14, 29])"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_label = np.array(list(map(old_author2new_author.__getitem__, train_y)), int)\n",
    "test_label = np.array(list(map(old_author2new_author.__getitem__, test_y)), int)\n",
    "test_label"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " embedding_2 (Embedding)     (None, None, 16)          121792    \n",
      "                                                                 \n",
      " global_average_pooling1d_2   (None, 16)               0         \n",
      " (GlobalAveragePooling1D)                                        \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 60)                1020      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 122,812\n",
      "Trainable params: 122,812\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras import regularizers\n",
    "import tensorflow_hub as hub\n",
    "\n",
    "# 输入形状是用于诗词的汉字数目（7611 词）\n",
    "vocab_size = 7611 + 1\n",
    "poets = 60\n",
    "\n",
    "model = keras.Sequential()\n",
    "# 第一维始终是batch的大小。\n",
    "model.add(keras.layers.Embedding(vocab_size, 16))  # 变成16个字\n",
    "# 官方解释：查找每个词索引的嵌入向量（embedding vector）\n",
    "# 嵌入向量是16维的。\n",
    "# 对于一系列batch的数据，把bx56 嵌入到b个 不知道多长的，每个单词变长16维向量的一个向量\n",
    "model.add(keras.layers.GlobalAveragePooling1D())  # 把变长的张量变长定长的？\n",
    "# 对于每一个 嵌入向量，每个单词16维，一句话不定长，直接把一句话的词向量求平均加起来。\n",
    "# model.add(keras.layers.Dense(poets * 16, activation='relu', kernel_regularizer=regularizers.l2(0.01)))\n",
    "# model.add(keras.layers.Dense(poets * 10, activation='relu'))\n",
    "# model.add(keras.layers.Dropout(0.2))\n",
    "# model.add(keras.layers.Dense(poets, activation='softmax', kernel_regularizer=regularizers.l2(0.01)))\n",
    "model.add(keras.layers.Dense(poets, activation='softmax', ))\n",
    "model.summary()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "outputs": [],
   "source": [
    "def categorical_squared_hinge(y_true, y_pred):\n",
    "    \"\"\"\n",
    "    hinge with 0.5*W^2 ,SVM\n",
    "    \"\"\"\n",
    "    y_true = 2. * y_true - 1  # trans [0,1] to [-1,1]，注意这个，svm类别标签是-1和1\n",
    "    vvvv = keras.maximum(1. - y_true * y_pred, 0.)  # hinge loss，参考keras自带的hinge loss\n",
    "    #    vvv = K.square(vvvv) # 文章《Deep Learning using Linear Support Vector Machines》有进行平方\n",
    "    vv = keras.sum(vvvv, 1, keepdims=False)  #axis=len(y_true.get_shape()) - 1\n",
    "    v = keras.mean(vv, axis=-1)\n",
    "    return v"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "batch_size = 16\n",
    "checkpoint_path = \"training_1/cp.ckpt\"\n",
    "checkpoint_dir = os.path.dirname(checkpoint_path)\n",
    "\n",
    "# Create a callback that saves the model's weights\n",
    "cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path,\n",
    "                                                 save_weights_only=True,\n",
    "                                                 verbose=1\n",
    "                                                 , save_freq=5 * batch_size)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "outputs": [],
   "source": [
    "# model.compile(optimizer='adam',\n",
    "#               loss=categorical_squared_hinge,\n",
    "#               metrics=['accuracy'])\n",
    "model.compile(optimizer='adam',\n",
    "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),\n",
    "              metrics=[tf.metrics.SparseCategoricalAccuracy()])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "outputs": [],
   "source": [
    "from keras.callbacks import EarlyStopping\n",
    "\n",
    "earlystop_callback = EarlyStopping(\n",
    "    monitor='val_accuracy', min_delta=0.0001,\n",
    "    patience=1)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10000\n",
      " 14/932 [..............................] - ETA: 3s - loss: 0.3429 - sparse_categorical_accuracy: 0.9420 \n",
      "Epoch 1: saving model to training_1\\cp.ckpt\n",
      " 95/932 [==>...........................] - ETA: 2s - loss: 0.4065 - sparse_categorical_accuracy: 0.9230\n",
      "Epoch 1: saving model to training_1\\cp.ckpt\n",
      "174/932 [====>.........................] - ETA: 2s - loss: 0.4323 - sparse_categorical_accuracy: 0.9199\n",
      "Epoch 1: saving model to training_1\\cp.ckpt\n",
      "250/932 [=======>......................] - ETA: 2s - loss: 0.4252 - sparse_categorical_accuracy: 0.9225\n",
      "Epoch 1: saving model to training_1\\cp.ckpt\n",
      "331/932 [=========>....................] - ETA: 2s - loss: 0.4295 - sparse_categorical_accuracy: 0.9213\n",
      "Epoch 1: saving model to training_1\\cp.ckpt\n",
      "416/932 [============>.................] - ETA: 1s - loss: 0.4392 - sparse_categorical_accuracy: 0.9195\n",
      "Epoch 1: saving model to training_1\\cp.ckpt\n",
      "499/932 [===============>..............] - ETA: 1s - loss: 0.4450 - sparse_categorical_accuracy: 0.9187\n",
      "Epoch 1: saving model to training_1\\cp.ckpt\n",
      "574/932 [=================>............] - ETA: 1s - loss: 0.4449 - sparse_categorical_accuracy: 0.9179\n",
      "Epoch 1: saving model to training_1\\cp.ckpt\n",
      "659/932 [====================>.........] - ETA: 0s - loss: 0.4469 - sparse_categorical_accuracy: 0.9176\n",
      "Epoch 1: saving model to training_1\\cp.ckpt\n",
      "730/932 [======================>.......] - ETA: 0s - loss: 0.4479 - sparse_categorical_accuracy: 0.9172\n",
      "Epoch 1: saving model to training_1\\cp.ckpt\n",
      "815/932 [=========================>....] - ETA: 0s - loss: 0.4473 - sparse_categorical_accuracy: 0.9166\n",
      "Epoch 1: saving model to training_1\\cp.ckpt\n",
      "898/932 [===========================>..] - ETA: 0s - loss: 0.4483 - sparse_categorical_accuracy: 0.9163\n",
      "Epoch 1: saving model to training_1\\cp.ckpt\n",
      "922/932 [============================>.] - ETA: 0s - loss: 0.4481 - sparse_categorical_accuracy: 0.9161WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 4s 4ms/step - loss: 0.4489 - sparse_categorical_accuracy: 0.9160 - val_loss: 5.3975 - val_sparse_categorical_accuracy: 0.2188\n",
      "Epoch 2/10000\n",
      " 35/932 [>.............................] - ETA: 2s - loss: 0.4597 - sparse_categorical_accuracy: 0.9232\n",
      "Epoch 2: saving model to training_1\\cp.ckpt\n",
      "121/932 [==>...........................] - ETA: 2s - loss: 0.4322 - sparse_categorical_accuracy: 0.9163\n",
      "Epoch 2: saving model to training_1\\cp.ckpt\n",
      "201/932 [=====>........................] - ETA: 2s - loss: 0.4277 - sparse_categorical_accuracy: 0.9220\n",
      "Epoch 2: saving model to training_1\\cp.ckpt\n",
      "282/932 [========>.....................] - ETA: 2s - loss: 0.4497 - sparse_categorical_accuracy: 0.9156\n",
      "Epoch 2: saving model to training_1\\cp.ckpt\n",
      "366/932 [==========>...................] - ETA: 1s - loss: 0.4409 - sparse_categorical_accuracy: 0.9203\n",
      "Epoch 2: saving model to training_1\\cp.ckpt\n",
      "432/932 [============>.................] - ETA: 1s - loss: 0.4387 - sparse_categorical_accuracy: 0.9194\n",
      "Epoch 2: saving model to training_1\\cp.ckpt\n",
      "526/932 [===============>..............] - ETA: 1s - loss: 0.4409 - sparse_categorical_accuracy: 0.9184\n",
      "Epoch 2: saving model to training_1\\cp.ckpt\n",
      "600/932 [==================>...........] - ETA: 1s - loss: 0.4458 - sparse_categorical_accuracy: 0.9155\n",
      "Epoch 2: saving model to training_1\\cp.ckpt\n",
      "687/932 [=====================>........] - ETA: 0s - loss: 0.4464 - sparse_categorical_accuracy: 0.9148\n",
      "Epoch 2: saving model to training_1\\cp.ckpt\n",
      "768/932 [=======================>......] - ETA: 0s - loss: 0.4427 - sparse_categorical_accuracy: 0.9162\n",
      "Epoch 2: saving model to training_1\\cp.ckpt\n",
      "844/932 [==========================>...] - ETA: 0s - loss: 0.4412 - sparse_categorical_accuracy: 0.9168\n",
      "Epoch 2: saving model to training_1\\cp.ckpt\n",
      "924/932 [============================>.] - ETA: 0s - loss: 0.4424 - sparse_categorical_accuracy: 0.9166\n",
      "Epoch 2: saving model to training_1\\cp.ckpt\n",
      "932/932 [==============================] - ETA: 0s - loss: 0.4438 - sparse_categorical_accuracy: 0.9161WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.4438 - sparse_categorical_accuracy: 0.9161 - val_loss: 5.4287 - val_sparse_categorical_accuracy: 0.2220\n",
      "Epoch 3/10000\n",
      " 74/932 [=>............................] - ETA: 2s - loss: 0.4158 - sparse_categorical_accuracy: 0.9231\n",
      "Epoch 3: saving model to training_1\\cp.ckpt\n",
      "153/932 [===>..........................] - ETA: 2s - loss: 0.4186 - sparse_categorical_accuracy: 0.9220\n",
      "Epoch 3: saving model to training_1\\cp.ckpt\n",
      "236/932 [======>.......................] - ETA: 2s - loss: 0.4238 - sparse_categorical_accuracy: 0.9203\n",
      "Epoch 3: saving model to training_1\\cp.ckpt\n",
      "301/932 [========>.....................] - ETA: 1s - loss: 0.4222 - sparse_categorical_accuracy: 0.9221\n",
      "Epoch 3: saving model to training_1\\cp.ckpt\n",
      "395/932 [===========>..................] - ETA: 1s - loss: 0.4183 - sparse_categorical_accuracy: 0.9231\n",
      "Epoch 3: saving model to training_1\\cp.ckpt\n",
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      "Epoch 3: saving model to training_1\\cp.ckpt\n",
      "920/932 [============================>.] - ETA: 0s - loss: 0.4395 - sparse_categorical_accuracy: 0.9185WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.4391 - sparse_categorical_accuracy: 0.9186 - val_loss: 5.4595 - val_sparse_categorical_accuracy: 0.2193\n",
      "Epoch 4/10000\n",
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      "Epoch 4: saving model to training_1\\cp.ckpt\n",
      "907/932 [============================>.] - ETA: 0s - loss: 0.4346 - sparse_categorical_accuracy: 0.9206\n",
      "Epoch 4: saving model to training_1\\cp.ckpt\n",
      "914/932 [============================>.] - ETA: 0s - loss: 0.4340 - sparse_categorical_accuracy: 0.9208WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.4340 - sparse_categorical_accuracy: 0.9206 - val_loss: 5.4881 - val_sparse_categorical_accuracy: 0.2193\n",
      "Epoch 5/10000\n",
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      "Epoch 5: saving model to training_1\\cp.ckpt\n",
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      "Epoch 5: saving model to training_1\\cp.ckpt\n",
      "932/932 [==============================] - ETA: 0s - loss: 0.4295 - sparse_categorical_accuracy: 0.9209WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.4295 - sparse_categorical_accuracy: 0.9209 - val_loss: 5.5230 - val_sparse_categorical_accuracy: 0.2183\n",
      "Epoch 6/10000\n",
      "  1/932 [..............................] - ETA: 3s - loss: 0.6608 - sparse_categorical_accuracy: 0.7500\n",
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      "Epoch 6: saving model to training_1\\cp.ckpt\n",
      "924/932 [============================>.] - ETA: 0s - loss: 0.4248 - sparse_categorical_accuracy: 0.9221WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 3ms/step - loss: 0.4248 - sparse_categorical_accuracy: 0.9221 - val_loss: 5.5513 - val_sparse_categorical_accuracy: 0.2174\n",
      "Epoch 7/10000\n",
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      "911/932 [============================>.] - ETA: 0s - loss: 0.4206 - sparse_categorical_accuracy: 0.9225\n",
      "Epoch 7: saving model to training_1\\cp.ckpt\n",
      "918/932 [============================>.] - ETA: 0s - loss: 0.4199 - sparse_categorical_accuracy: 0.9228WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.4196 - sparse_categorical_accuracy: 0.9228 - val_loss: 5.5858 - val_sparse_categorical_accuracy: 0.2183\n",
      "Epoch 8/10000\n",
      " 57/932 [>.............................] - ETA: 2s - loss: 0.3843 - sparse_categorical_accuracy: 0.9309\n",
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      "696/932 [=====================>........] - ETA: 0s - loss: 0.4130 - sparse_categorical_accuracy: 0.9248\n",
      "Epoch 8: saving model to training_1\\cp.ckpt\n",
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      "Epoch 8: saving model to training_1\\cp.ckpt\n",
      "917/932 [============================>.] - ETA: 0s - loss: 0.4151 - sparse_categorical_accuracy: 0.9239WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 3ms/step - loss: 0.4150 - sparse_categorical_accuracy: 0.9242 - val_loss: 5.6178 - val_sparse_categorical_accuracy: 0.2158\n",
      "Epoch 9/10000\n",
      "  1/932 [..............................] - ETA: 3s - loss: 0.3756 - sparse_categorical_accuracy: 1.0000\n",
      "Epoch 9: saving model to training_1\\cp.ckpt\n",
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      "Epoch 9: saving model to training_1\\cp.ckpt\n",
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      "Epoch 9: saving model to training_1\\cp.ckpt\n",
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      "Epoch 9: saving model to training_1\\cp.ckpt\n",
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      "Epoch 9: saving model to training_1\\cp.ckpt\n",
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      "Epoch 9: saving model to training_1\\cp.ckpt\n",
      "886/932 [===========================>..] - ETA: 0s - loss: 0.4094 - sparse_categorical_accuracy: 0.9262\n",
      "Epoch 9: saving model to training_1\\cp.ckpt\n",
      "929/932 [============================>.] - ETA: 0s - loss: 0.4106 - sparse_categorical_accuracy: 0.9261WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 3ms/step - loss: 0.4106 - sparse_categorical_accuracy: 0.9262 - val_loss: 5.6530 - val_sparse_categorical_accuracy: 0.2169\n",
      "Epoch 10/10000\n",
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      "Epoch 10: saving model to training_1\\cp.ckpt\n",
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      "Epoch 10: saving model to training_1\\cp.ckpt\n",
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      "Epoch 10: saving model to training_1\\cp.ckpt\n",
      "659/932 [====================>.........] - ETA: 0s - loss: 0.4032 - sparse_categorical_accuracy: 0.9280\n",
      "Epoch 10: saving model to training_1\\cp.ckpt\n",
      "752/932 [=======================>......] - ETA: 0s - loss: 0.4016 - sparse_categorical_accuracy: 0.9285\n",
      "Epoch 10: saving model to training_1\\cp.ckpt\n",
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      "Epoch 10: saving model to training_1\\cp.ckpt\n",
      "914/932 [============================>.] - ETA: 0s - loss: 0.4069 - sparse_categorical_accuracy: 0.9265\n",
      "Epoch 10: saving model to training_1\\cp.ckpt\n",
      "922/932 [============================>.] - ETA: 0s - loss: 0.4059 - sparse_categorical_accuracy: 0.9267WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 3ms/step - loss: 0.4058 - sparse_categorical_accuracy: 0.9268 - val_loss: 5.6818 - val_sparse_categorical_accuracy: 0.2140\n",
      "Epoch 11/10000\n",
      " 58/932 [>.............................] - ETA: 2s - loss: 0.4077 - sparse_categorical_accuracy: 0.9300\n",
      "Epoch 11: saving model to training_1\\cp.ckpt\n",
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      "Epoch 11: saving model to training_1\\cp.ckpt\n",
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      "Epoch 11: saving model to training_1\\cp.ckpt\n",
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      "Epoch 11: saving model to training_1\\cp.ckpt\n",
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      "Epoch 11: saving model to training_1\\cp.ckpt\n",
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      "Epoch 11: saving model to training_1\\cp.ckpt\n",
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      "Epoch 11: saving model to training_1\\cp.ckpt\n",
      "701/932 [=====================>........] - ETA: 0s - loss: 0.3908 - sparse_categorical_accuracy: 0.9313\n",
      "Epoch 11: saving model to training_1\\cp.ckpt\n",
      "767/932 [=======================>......] - ETA: 0s - loss: 0.3932 - sparse_categorical_accuracy: 0.9306\n",
      "Epoch 11: saving model to training_1\\cp.ckpt\n",
      "858/932 [==========================>...] - ETA: 0s - loss: 0.3955 - sparse_categorical_accuracy: 0.9303\n",
      "Epoch 11: saving model to training_1\\cp.ckpt\n",
      "925/932 [============================>.] - ETA: 0s - loss: 0.4010 - sparse_categorical_accuracy: 0.9291WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 3ms/step - loss: 0.4012 - sparse_categorical_accuracy: 0.9289 - val_loss: 5.7190 - val_sparse_categorical_accuracy: 0.2174\n",
      "Epoch 12/10000\n",
      "  1/932 [..............................] - ETA: 4s - loss: 0.3347 - sparse_categorical_accuracy: 0.9375\n",
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      "Epoch 12: saving model to training_1\\cp.ckpt\n",
      "916/932 [============================>.] - ETA: 0s - loss: 0.3965 - sparse_categorical_accuracy: 0.9294WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 3ms/step - loss: 0.3968 - sparse_categorical_accuracy: 0.9296 - val_loss: 5.7492 - val_sparse_categorical_accuracy: 0.2164\n",
      "Epoch 13/10000\n",
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      "Epoch 13: saving model to training_1\\cp.ckpt\n",
      "925/932 [============================>.] - ETA: 0s - loss: 0.3924 - sparse_categorical_accuracy: 0.9293WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 3ms/step - loss: 0.3922 - sparse_categorical_accuracy: 0.9294 - val_loss: 5.7797 - val_sparse_categorical_accuracy: 0.2166\n",
      "Epoch 14/10000\n",
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      "928/932 [============================>.] - ETA: 0s - loss: 0.3881 - sparse_categorical_accuracy: 0.9309WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 3ms/step - loss: 0.3878 - sparse_categorical_accuracy: 0.9310 - val_loss: 5.8160 - val_sparse_categorical_accuracy: 0.2161\n",
      "Epoch 15/10000\n",
      "  1/932 [..............................] - ETA: 4s - loss: 0.2970 - sparse_categorical_accuracy: 1.0000\n",
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      "914/932 [============================>.] - ETA: 0s - loss: 0.3837 - sparse_categorical_accuracy: 0.9333WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 3ms/step - loss: 0.3834 - sparse_categorical_accuracy: 0.9332 - val_loss: 5.8457 - val_sparse_categorical_accuracy: 0.2156\n",
      "Epoch 16/10000\n",
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      "929/932 [============================>.] - ETA: 0s - loss: 0.3794 - sparse_categorical_accuracy: 0.9338WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 3ms/step - loss: 0.3790 - sparse_categorical_accuracy: 0.9338 - val_loss: 5.8770 - val_sparse_categorical_accuracy: 0.2137\n",
      "Epoch 17/10000\n",
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      "Epoch 17: saving model to training_1\\cp.ckpt\n",
      "914/932 [============================>.] - ETA: 0s - loss: 0.3749 - sparse_categorical_accuracy: 0.9350WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.3748 - sparse_categorical_accuracy: 0.9350 - val_loss: 5.9106 - val_sparse_categorical_accuracy: 0.2156\n",
      "Epoch 18/10000\n",
      "  1/932 [..............................] - ETA: 3s - loss: 0.3076 - sparse_categorical_accuracy: 0.9375\n",
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      "Epoch 18: saving model to training_1\\cp.ckpt\n",
      "919/932 [============================>.] - ETA: 0s - loss: 0.3707 - sparse_categorical_accuracy: 0.9357WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 3ms/step - loss: 0.3703 - sparse_categorical_accuracy: 0.9358 - val_loss: 5.9469 - val_sparse_categorical_accuracy: 0.2156\n",
      "Epoch 19/10000\n",
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      "Epoch 19: saving model to training_1\\cp.ckpt\n",
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      "Epoch 19: saving model to training_1\\cp.ckpt\n",
      "925/932 [============================>.] - ETA: 0s - loss: 0.3664 - sparse_categorical_accuracy: 0.9373\n",
      "Epoch 19: saving model to training_1\\cp.ckpt\n",
      "WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 3ms/step - loss: 0.3662 - sparse_categorical_accuracy: 0.9374 - val_loss: 5.9771 - val_sparse_categorical_accuracy: 0.2123\n",
      "Epoch 20/10000\n",
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      "Epoch 20: saving model to training_1\\cp.ckpt\n",
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      "Epoch 20: saving model to training_1\\cp.ckpt\n",
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      "Epoch 20: saving model to training_1\\cp.ckpt\n",
      "921/932 [============================>.] - ETA: 0s - loss: 0.3628 - sparse_categorical_accuracy: 0.9370WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 3ms/step - loss: 0.3622 - sparse_categorical_accuracy: 0.9374 - val_loss: 6.0129 - val_sparse_categorical_accuracy: 0.2140\n",
      "Epoch 21/10000\n",
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      "Epoch 21: saving model to training_1\\cp.ckpt\n",
      "738/932 [======================>.......] - ETA: 0s - loss: 0.3581 - sparse_categorical_accuracy: 0.9387\n",
      "Epoch 21: saving model to training_1\\cp.ckpt\n",
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      "Epoch 21: saving model to training_1\\cp.ckpt\n",
      "922/932 [============================>.] - ETA: 0s - loss: 0.3584 - sparse_categorical_accuracy: 0.9391WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.3578 - sparse_categorical_accuracy: 0.9391 - val_loss: 6.0463 - val_sparse_categorical_accuracy: 0.2148\n",
      "Epoch 22/10000\n",
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      "Epoch 22: saving model to training_1\\cp.ckpt\n",
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      "Epoch 22: saving model to training_1\\cp.ckpt\n",
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      "Epoch 22: saving model to training_1\\cp.ckpt\n",
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      "929/932 [============================>.] - ETA: 0s - loss: 0.3540 - sparse_categorical_accuracy: 0.9400\n",
      "Epoch 22: saving model to training_1\\cp.ckpt\n",
      "WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 3ms/step - loss: 0.3537 - sparse_categorical_accuracy: 0.9401 - val_loss: 6.0794 - val_sparse_categorical_accuracy: 0.2148\n",
      "Epoch 23/10000\n",
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      "Epoch 23: saving model to training_1\\cp.ckpt\n",
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      "Epoch 23: saving model to training_1\\cp.ckpt\n",
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      "Epoch 23: saving model to training_1\\cp.ckpt\n",
      "876/932 [===========================>..] - ETA: 0s - loss: 0.3509 - sparse_categorical_accuracy: 0.9406\n",
      "Epoch 23: saving model to training_1\\cp.ckpt\n",
      "918/932 [============================>.] - ETA: 0s - loss: 0.3505 - sparse_categorical_accuracy: 0.9413WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.3498 - sparse_categorical_accuracy: 0.9415 - val_loss: 6.1151 - val_sparse_categorical_accuracy: 0.2132\n",
      "Epoch 24/10000\n",
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      "Epoch 24: saving model to training_1\\cp.ckpt\n",
      "931/932 [============================>.] - ETA: 0s - loss: 0.3456 - sparse_categorical_accuracy: 0.9432WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 3ms/step - loss: 0.3459 - sparse_categorical_accuracy: 0.9431 - val_loss: 6.1486 - val_sparse_categorical_accuracy: 0.2137\n",
      "Epoch 25/10000\n",
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      "Epoch 25: saving model to training_1\\cp.ckpt\n",
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      "Epoch 25: saving model to training_1\\cp.ckpt\n",
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      "Epoch 25: saving model to training_1\\cp.ckpt\n",
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      "Epoch 25: saving model to training_1\\cp.ckpt\n",
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      "Epoch 25: saving model to training_1\\cp.ckpt\n",
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      "Epoch 25: saving model to training_1\\cp.ckpt\n",
      "919/932 [============================>.] - ETA: 0s - loss: 0.3413 - sparse_categorical_accuracy: 0.9433WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 3ms/step - loss: 0.3419 - sparse_categorical_accuracy: 0.9431 - val_loss: 6.1878 - val_sparse_categorical_accuracy: 0.2137\n",
      "Epoch 26/10000\n",
      "  1/932 [..............................] - ETA: 3s - loss: 0.1050 - sparse_categorical_accuracy: 1.0000\n",
      "Epoch 26: saving model to training_1\\cp.ckpt\n",
      " 66/932 [=>............................] - ETA: 2s - loss: 0.3471 - sparse_categorical_accuracy: 0.9451\n",
      "Epoch 26: saving model to training_1\\cp.ckpt\n",
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      "Epoch 26: saving model to training_1\\cp.ckpt\n",
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      "Epoch 26: saving model to training_1\\cp.ckpt\n",
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      "Epoch 26: saving model to training_1\\cp.ckpt\n",
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      "Epoch 26: saving model to training_1\\cp.ckpt\n",
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      "Epoch 26: saving model to training_1\\cp.ckpt\n",
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      "Epoch 26: saving model to training_1\\cp.ckpt\n",
      "883/932 [===========================>..] - ETA: 0s - loss: 0.3376 - sparse_categorical_accuracy: 0.9437\n",
      "Epoch 26: saving model to training_1\\cp.ckpt\n",
      "926/932 [============================>.] - ETA: 0s - loss: 0.3378 - sparse_categorical_accuracy: 0.9436WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 3ms/step - loss: 0.3379 - sparse_categorical_accuracy: 0.9436 - val_loss: 6.2174 - val_sparse_categorical_accuracy: 0.2126\n",
      "Epoch 27/10000\n",
      " 20/932 [..............................] - ETA: 2s - loss: 0.3128 - sparse_categorical_accuracy: 0.9531\n",
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      "Epoch 27: saving model to training_1\\cp.ckpt\n",
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      "Epoch 27: saving model to training_1\\cp.ckpt\n",
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      "Epoch 27: saving model to training_1\\cp.ckpt\n",
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      "Epoch 27: saving model to training_1\\cp.ckpt\n",
      "511/932 [===============>..............] - ETA: 1s - loss: 0.3304 - sparse_categorical_accuracy: 0.9461\n",
      "Epoch 27: saving model to training_1\\cp.ckpt\n",
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      "Epoch 27: saving model to training_1\\cp.ckpt\n",
      "663/932 [====================>.........] - ETA: 0s - loss: 0.3345 - sparse_categorical_accuracy: 0.9448\n",
      "Epoch 27: saving model to training_1\\cp.ckpt\n",
      "738/932 [======================>.......] - ETA: 0s - loss: 0.3363 - sparse_categorical_accuracy: 0.9439\n",
      "Epoch 27: saving model to training_1\\cp.ckpt\n",
      "825/932 [=========================>....] - ETA: 0s - loss: 0.3349 - sparse_categorical_accuracy: 0.9448\n",
      "Epoch 27: saving model to training_1\\cp.ckpt\n",
      "906/932 [============================>.] - ETA: 0s - loss: 0.3338 - sparse_categorical_accuracy: 0.9447\n",
      "Epoch 27: saving model to training_1\\cp.ckpt\n",
      "931/932 [============================>.] - ETA: 0s - loss: 0.3337 - sparse_categorical_accuracy: 0.9451WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.3337 - sparse_categorical_accuracy: 0.9451 - val_loss: 6.2511 - val_sparse_categorical_accuracy: 0.2113\n",
      "Epoch 28/10000\n",
      " 58/932 [>.............................] - ETA: 2s - loss: 0.3140 - sparse_categorical_accuracy: 0.9461\n",
      "Epoch 28: saving model to training_1\\cp.ckpt\n",
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      "Epoch 28: saving model to training_1\\cp.ckpt\n",
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      "Epoch 28: saving model to training_1\\cp.ckpt\n",
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      "Epoch 28: saving model to training_1\\cp.ckpt\n",
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      "Epoch 28: saving model to training_1\\cp.ckpt\n",
      "442/932 [=============>................] - ETA: 1s - loss: 0.3245 - sparse_categorical_accuracy: 0.9467\n",
      "Epoch 28: saving model to training_1\\cp.ckpt\n",
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      "Epoch 28: saving model to training_1\\cp.ckpt\n",
      "603/932 [==================>...........] - ETA: 1s - loss: 0.3273 - sparse_categorical_accuracy: 0.9462\n",
      "Epoch 28: saving model to training_1\\cp.ckpt\n",
      "697/932 [=====================>........] - ETA: 0s - loss: 0.3286 - sparse_categorical_accuracy: 0.9461\n",
      "Epoch 28: saving model to training_1\\cp.ckpt\n",
      "779/932 [========================>.....] - ETA: 0s - loss: 0.3290 - sparse_categorical_accuracy: 0.9459\n",
      "Epoch 28: saving model to training_1\\cp.ckpt\n",
      "846/932 [==========================>...] - ETA: 0s - loss: 0.3298 - sparse_categorical_accuracy: 0.9453\n",
      "Epoch 28: saving model to training_1\\cp.ckpt\n",
      "916/932 [============================>.] - ETA: 0s - loss: 0.3303 - sparse_categorical_accuracy: 0.9453WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 3ms/step - loss: 0.3300 - sparse_categorical_accuracy: 0.9454 - val_loss: 6.2868 - val_sparse_categorical_accuracy: 0.2129\n",
      "Epoch 29/10000\n",
      "  1/932 [..............................] - ETA: 4s - loss: 0.1669 - sparse_categorical_accuracy: 1.0000\n",
      "Epoch 29: saving model to training_1\\cp.ckpt\n",
      " 85/932 [=>............................] - ETA: 2s - loss: 0.2882 - sparse_categorical_accuracy: 0.9610\n",
      "Epoch 29: saving model to training_1\\cp.ckpt\n",
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      "Epoch 29: saving model to training_1\\cp.ckpt\n",
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      "928/932 [============================>.] - ETA: 0s - loss: 0.3260 - sparse_categorical_accuracy: 0.9475WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 3ms/step - loss: 0.3264 - sparse_categorical_accuracy: 0.9475 - val_loss: 6.3211 - val_sparse_categorical_accuracy: 0.2107\n",
      "Epoch 30/10000\n",
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      "919/932 [============================>.] - ETA: 0s - loss: 0.3225 - sparse_categorical_accuracy: 0.9488WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 3ms/step - loss: 0.3224 - sparse_categorical_accuracy: 0.9487 - val_loss: 6.3552 - val_sparse_categorical_accuracy: 0.2115\n",
      "Epoch 31/10000\n",
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      "922/932 [============================>.] - ETA: 0s - loss: 0.3183 - sparse_categorical_accuracy: 0.9501WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.3188 - sparse_categorical_accuracy: 0.9499 - val_loss: 6.3903 - val_sparse_categorical_accuracy: 0.2123\n",
      "Epoch 32/10000\n",
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      "917/932 [============================>.] - ETA: 0s - loss: 0.3141 - sparse_categorical_accuracy: 0.9502WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 3ms/step - loss: 0.3151 - sparse_categorical_accuracy: 0.9500 - val_loss: 6.4283 - val_sparse_categorical_accuracy: 0.2148\n",
      "Epoch 33/10000\n",
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      "924/932 [============================>.] - ETA: 0s - loss: 0.3106 - sparse_categorical_accuracy: 0.9510WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 3ms/step - loss: 0.3113 - sparse_categorical_accuracy: 0.9510 - val_loss: 6.4686 - val_sparse_categorical_accuracy: 0.2164\n",
      "Epoch 34/10000\n",
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      "926/932 [============================>.] - ETA: 0s - loss: 0.3077 - sparse_categorical_accuracy: 0.9527WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 3ms/step - loss: 0.3078 - sparse_categorical_accuracy: 0.9525 - val_loss: 6.4971 - val_sparse_categorical_accuracy: 0.2145\n",
      "Epoch 35/10000\n",
      "  1/932 [..............................] - ETA: 3s - loss: 0.1961 - sparse_categorical_accuracy: 1.0000\n",
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      "916/932 [============================>.] - ETA: 0s - loss: 0.3036 - sparse_categorical_accuracy: 0.9531WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 3ms/step - loss: 0.3039 - sparse_categorical_accuracy: 0.9531 - val_loss: 6.5372 - val_sparse_categorical_accuracy: 0.2113\n",
      "Epoch 36/10000\n",
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      "916/932 [============================>.] - ETA: 0s - loss: 0.3003 - sparse_categorical_accuracy: 0.9539\n",
      "Epoch 36: saving model to training_1\\cp.ckpt\n",
      "924/932 [============================>.] - ETA: 0s - loss: 0.3005 - sparse_categorical_accuracy: 0.9538WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.3005 - sparse_categorical_accuracy: 0.9537 - val_loss: 6.5680 - val_sparse_categorical_accuracy: 0.2083\n",
      "Epoch 37/10000\n",
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      "Epoch 37: saving model to training_1\\cp.ckpt\n",
      "914/932 [============================>.] - ETA: 0s - loss: 0.2963 - sparse_categorical_accuracy: 0.9559WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 3ms/step - loss: 0.2968 - sparse_categorical_accuracy: 0.9555 - val_loss: 6.6041 - val_sparse_categorical_accuracy: 0.2137\n",
      "Epoch 38/10000\n",
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      "899/932 [===========================>..] - ETA: 0s - loss: 0.2913 - sparse_categorical_accuracy: 0.9554\n",
      "Epoch 38: saving model to training_1\\cp.ckpt\n",
      "926/932 [============================>.] - ETA: 0s - loss: 0.2935 - sparse_categorical_accuracy: 0.9549WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 3ms/step - loss: 0.2932 - sparse_categorical_accuracy: 0.9550 - val_loss: 6.6380 - val_sparse_categorical_accuracy: 0.2089\n",
      "Epoch 39/10000\n",
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      "926/932 [============================>.] - ETA: 0s - loss: 0.2900 - sparse_categorical_accuracy: 0.9563\n",
      "Epoch 39: saving model to training_1\\cp.ckpt\n",
      "931/932 [============================>.] - ETA: 0s - loss: 0.2898 - sparse_categorical_accuracy: 0.9562WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 3ms/step - loss: 0.2898 - sparse_categorical_accuracy: 0.9562 - val_loss: 6.6774 - val_sparse_categorical_accuracy: 0.2097\n",
      "Epoch 40/10000\n",
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      "Epoch 40: saving model to training_1\\cp.ckpt\n",
      "918/932 [============================>.] - ETA: 0s - loss: 0.2865 - sparse_categorical_accuracy: 0.9568WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.2862 - sparse_categorical_accuracy: 0.9569 - val_loss: 6.7161 - val_sparse_categorical_accuracy: 0.2089\n",
      "Epoch 41/10000\n",
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      "Epoch 41: saving model to training_1\\cp.ckpt\n",
      "926/932 [============================>.] - ETA: 0s - loss: 0.2831 - sparse_categorical_accuracy: 0.9573WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.2830 - sparse_categorical_accuracy: 0.9573 - val_loss: 6.7498 - val_sparse_categorical_accuracy: 0.2097\n",
      "Epoch 42/10000\n",
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      "926/932 [============================>.] - ETA: 0s - loss: 0.2797 - sparse_categorical_accuracy: 0.9592\n",
      "Epoch 42: saving model to training_1\\cp.ckpt\n",
      "WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.2794 - sparse_categorical_accuracy: 0.9593 - val_loss: 6.7838 - val_sparse_categorical_accuracy: 0.2081\n",
      "Epoch 43/10000\n",
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      "Epoch 43: saving model to training_1\\cp.ckpt\n",
      "917/932 [============================>.] - ETA: 0s - loss: 0.2755 - sparse_categorical_accuracy: 0.9603WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 3ms/step - loss: 0.2763 - sparse_categorical_accuracy: 0.9600 - val_loss: 6.8205 - val_sparse_categorical_accuracy: 0.2083\n",
      "Epoch 44/10000\n",
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      "904/932 [============================>.] - ETA: 0s - loss: 0.2722 - sparse_categorical_accuracy: 0.9604\n",
      "Epoch 44: saving model to training_1\\cp.ckpt\n",
      "930/932 [============================>.] - ETA: 0s - loss: 0.2730 - sparse_categorical_accuracy: 0.9601WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.2730 - sparse_categorical_accuracy: 0.9602 - val_loss: 6.8582 - val_sparse_categorical_accuracy: 0.2105\n",
      "Epoch 45/10000\n",
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      "Epoch 45: saving model to training_1\\cp.ckpt\n",
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      "Epoch 45: saving model to training_1\\cp.ckpt\n",
      "916/932 [============================>.] - ETA: 0s - loss: 0.2697 - sparse_categorical_accuracy: 0.9615WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 3ms/step - loss: 0.2696 - sparse_categorical_accuracy: 0.9614 - val_loss: 6.8959 - val_sparse_categorical_accuracy: 0.2110\n",
      "Epoch 46/10000\n",
      "  1/932 [..............................] - ETA: 4s - loss: 0.3117 - sparse_categorical_accuracy: 0.9375\n",
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      "Epoch 46: saving model to training_1\\cp.ckpt\n",
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      "Epoch 46: saving model to training_1\\cp.ckpt\n",
      "926/932 [============================>.] - ETA: 0s - loss: 0.2659 - sparse_categorical_accuracy: 0.9617WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 3ms/step - loss: 0.2661 - sparse_categorical_accuracy: 0.9617 - val_loss: 6.9341 - val_sparse_categorical_accuracy: 0.2078\n",
      "Epoch 47/10000\n",
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      "Epoch 47: saving model to training_1\\cp.ckpt\n",
      "916/932 [============================>.] - ETA: 0s - loss: 0.2637 - sparse_categorical_accuracy: 0.9614WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 3ms/step - loss: 0.2630 - sparse_categorical_accuracy: 0.9616 - val_loss: 6.9711 - val_sparse_categorical_accuracy: 0.2097\n",
      "Epoch 48/10000\n",
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      "Epoch 48: saving model to training_1\\cp.ckpt\n",
      "925/932 [============================>.] - ETA: 0s - loss: 0.2594 - sparse_categorical_accuracy: 0.9632WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 3ms/step - loss: 0.2600 - sparse_categorical_accuracy: 0.9631 - val_loss: 7.0059 - val_sparse_categorical_accuracy: 0.2099\n",
      "Epoch 49/10000\n",
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      "Epoch 49: saving model to training_1\\cp.ckpt\n",
      "929/932 [============================>.] - ETA: 0s - loss: 0.2565 - sparse_categorical_accuracy: 0.9633WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 3ms/step - loss: 0.2566 - sparse_categorical_accuracy: 0.9632 - val_loss: 7.0441 - val_sparse_categorical_accuracy: 0.2081\n",
      "Epoch 50/10000\n",
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      "915/932 [============================>.] - ETA: 0s - loss: 0.2521 - sparse_categorical_accuracy: 0.9650\n",
      "Epoch 50: saving model to training_1\\cp.ckpt\n",
      "923/932 [============================>.] - ETA: 0s - loss: 0.2530 - sparse_categorical_accuracy: 0.9647WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 3ms/step - loss: 0.2535 - sparse_categorical_accuracy: 0.9644 - val_loss: 7.0790 - val_sparse_categorical_accuracy: 0.2091\n",
      "Epoch 51/10000\n",
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      "Epoch 51: saving model to training_1\\cp.ckpt\n",
      "920/932 [============================>.] - ETA: 0s - loss: 0.2504 - sparse_categorical_accuracy: 0.9650WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 3ms/step - loss: 0.2503 - sparse_categorical_accuracy: 0.9650 - val_loss: 7.1179 - val_sparse_categorical_accuracy: 0.2083\n",
      "Epoch 52/10000\n",
      "  1/932 [..............................] - ETA: 4s - loss: 0.2216 - sparse_categorical_accuracy: 0.9375\n",
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      "Epoch 52: saving model to training_1\\cp.ckpt\n",
      "730/932 [======================>.......] - ETA: 0s - loss: 0.2464 - sparse_categorical_accuracy: 0.9653\n",
      "Epoch 52: saving model to training_1\\cp.ckpt\n",
      "793/932 [========================>.....] - ETA: 0s - loss: 0.2461 - sparse_categorical_accuracy: 0.9656\n",
      "Epoch 52: saving model to training_1\\cp.ckpt\n",
      "889/932 [===========================>..] - ETA: 0s - loss: 0.2470 - sparse_categorical_accuracy: 0.9657\n",
      "Epoch 52: saving model to training_1\\cp.ckpt\n",
      "916/932 [============================>.] - ETA: 0s - loss: 0.2466 - sparse_categorical_accuracy: 0.9656WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 3ms/step - loss: 0.2469 - sparse_categorical_accuracy: 0.9654 - val_loss: 7.1575 - val_sparse_categorical_accuracy: 0.2083\n",
      "Epoch 53/10000\n",
      " 39/932 [>.............................] - ETA: 2s - loss: 0.2355 - sparse_categorical_accuracy: 0.9663\n",
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      "Epoch 53: saving model to training_1\\cp.ckpt\n",
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      "Epoch 53: saving model to training_1\\cp.ckpt\n",
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      "677/932 [====================>.........] - ETA: 0s - loss: 0.2401 - sparse_categorical_accuracy: 0.9672\n",
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      "Epoch 53: saving model to training_1\\cp.ckpt\n",
      "906/932 [============================>.] - ETA: 0s - loss: 0.2436 - sparse_categorical_accuracy: 0.9660\n",
      "Epoch 53: saving model to training_1\\cp.ckpt\n",
      "920/932 [============================>.] - ETA: 0s - loss: 0.2436 - sparse_categorical_accuracy: 0.9660WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 3ms/step - loss: 0.2442 - sparse_categorical_accuracy: 0.9658 - val_loss: 7.1923 - val_sparse_categorical_accuracy: 0.2062\n",
      "Epoch 54/10000\n",
      " 55/932 [>.............................] - ETA: 2s - loss: 0.2185 - sparse_categorical_accuracy: 0.9693\n",
      "Epoch 54: saving model to training_1\\cp.ckpt\n",
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      "Epoch 54: saving model to training_1\\cp.ckpt\n",
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      "Epoch 54: saving model to training_1\\cp.ckpt\n",
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      "Epoch 54: saving model to training_1\\cp.ckpt\n",
      "452/932 [=============>................] - ETA: 1s - loss: 0.2292 - sparse_categorical_accuracy: 0.9707\n",
      "Epoch 54: saving model to training_1\\cp.ckpt\n",
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      "Epoch 54: saving model to training_1\\cp.ckpt\n",
      "627/932 [===================>..........] - ETA: 0s - loss: 0.2377 - sparse_categorical_accuracy: 0.9686\n",
      "Epoch 54: saving model to training_1\\cp.ckpt\n",
      "691/932 [=====================>........] - ETA: 0s - loss: 0.2393 - sparse_categorical_accuracy: 0.9683\n",
      "Epoch 54: saving model to training_1\\cp.ckpt\n",
      "784/932 [========================>.....] - ETA: 0s - loss: 0.2377 - sparse_categorical_accuracy: 0.9684\n",
      "Epoch 54: saving model to training_1\\cp.ckpt\n",
      "867/932 [==========================>...] - ETA: 0s - loss: 0.2407 - sparse_categorical_accuracy: 0.9676\n",
      "Epoch 54: saving model to training_1\\cp.ckpt\n",
      "914/932 [============================>.] - ETA: 0s - loss: 0.2414 - sparse_categorical_accuracy: 0.9672WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 3ms/step - loss: 0.2415 - sparse_categorical_accuracy: 0.9672 - val_loss: 7.2346 - val_sparse_categorical_accuracy: 0.2081\n",
      "Epoch 55/10000\n",
      "  1/932 [..............................] - ETA: 4s - loss: 0.1698 - sparse_categorical_accuracy: 1.0000\n",
      "Epoch 55: saving model to training_1\\cp.ckpt\n",
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      "Epoch 55: saving model to training_1\\cp.ckpt\n",
      "915/932 [============================>.] - ETA: 0s - loss: 0.2371 - sparse_categorical_accuracy: 0.9682WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 3ms/step - loss: 0.2382 - sparse_categorical_accuracy: 0.9679 - val_loss: 7.2726 - val_sparse_categorical_accuracy: 0.2072\n",
      "Epoch 56/10000\n",
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      "924/932 [============================>.] - ETA: 0s - loss: 0.2348 - sparse_categorical_accuracy: 0.9685WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 3ms/step - loss: 0.2351 - sparse_categorical_accuracy: 0.9685 - val_loss: 7.3090 - val_sparse_categorical_accuracy: 0.2094\n",
      "Epoch 57/10000\n",
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      "Epoch 57: saving model to training_1\\cp.ckpt\n",
      "930/932 [============================>.] - ETA: 0s - loss: 0.2320 - sparse_categorical_accuracy: 0.9683WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 3ms/step - loss: 0.2323 - sparse_categorical_accuracy: 0.9683 - val_loss: 7.3453 - val_sparse_categorical_accuracy: 0.2070\n",
      "Epoch 58/10000\n",
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      "919/932 [============================>.] - ETA: 0s - loss: 0.2295 - sparse_categorical_accuracy: 0.9697WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 3ms/step - loss: 0.2294 - sparse_categorical_accuracy: 0.9697 - val_loss: 7.3878 - val_sparse_categorical_accuracy: 0.2054\n",
      "Epoch 59/10000\n",
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      "927/932 [============================>.] - ETA: 0s - loss: 0.2261 - sparse_categorical_accuracy: 0.9707\n",
      "Epoch 59: saving model to training_1\\cp.ckpt\n",
      "WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 3ms/step - loss: 0.2264 - sparse_categorical_accuracy: 0.9705 - val_loss: 7.4259 - val_sparse_categorical_accuracy: 0.2054\n",
      "Epoch 60/10000\n",
      " 56/932 [>.............................] - ETA: 2s - loss: 0.2217 - sparse_categorical_accuracy: 0.9676\n",
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      "914/932 [============================>.] - ETA: 0s - loss: 0.2228 - sparse_categorical_accuracy: 0.9712WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 3ms/step - loss: 0.2235 - sparse_categorical_accuracy: 0.9708 - val_loss: 7.4658 - val_sparse_categorical_accuracy: 0.2062\n",
      "Epoch 61/10000\n",
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      "Epoch 61: saving model to training_1\\cp.ckpt\n",
      "923/932 [============================>.] - ETA: 0s - loss: 0.2204 - sparse_categorical_accuracy: 0.9716WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 3ms/step - loss: 0.2210 - sparse_categorical_accuracy: 0.9715 - val_loss: 7.5019 - val_sparse_categorical_accuracy: 0.2062\n",
      "Epoch 62/10000\n",
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      "928/932 [============================>.] - ETA: 0s - loss: 0.2180 - sparse_categorical_accuracy: 0.9723\n",
      "Epoch 62: saving model to training_1\\cp.ckpt\n",
      "WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 3ms/step - loss: 0.2181 - sparse_categorical_accuracy: 0.9723 - val_loss: 7.5438 - val_sparse_categorical_accuracy: 0.2089\n",
      "Epoch 63/10000\n",
      " 78/932 [=>............................] - ETA: 2s - loss: 0.1907 - sparse_categorical_accuracy: 0.9760\n",
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      "795/932 [========================>.....] - ETA: 0s - loss: 0.2139 - sparse_categorical_accuracy: 0.9731\n",
      "Epoch 63: saving model to training_1\\cp.ckpt\n",
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      "Epoch 63: saving model to training_1\\cp.ckpt\n",
      "924/932 [============================>.] - ETA: 0s - loss: 0.2150 - sparse_categorical_accuracy: 0.9727WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 3ms/step - loss: 0.2154 - sparse_categorical_accuracy: 0.9725 - val_loss: 7.5782 - val_sparse_categorical_accuracy: 0.2059\n",
      "Epoch 64/10000\n",
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      "914/932 [============================>.] - ETA: 0s - loss: 0.2123 - sparse_categorical_accuracy: 0.9731WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 3ms/step - loss: 0.2125 - sparse_categorical_accuracy: 0.9729 - val_loss: 7.6213 - val_sparse_categorical_accuracy: 0.2046\n",
      "Epoch 65/10000\n",
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      "Epoch 65: saving model to training_1\\cp.ckpt\n",
      "931/932 [============================>.] - ETA: 0s - loss: 0.2097 - sparse_categorical_accuracy: 0.9735WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.2097 - sparse_categorical_accuracy: 0.9736 - val_loss: 7.6637 - val_sparse_categorical_accuracy: 0.2078\n",
      "Epoch 66/10000\n",
      "  1/932 [..............................] - ETA: 3s - loss: 0.0814 - sparse_categorical_accuracy: 1.0000\n",
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      "922/932 [============================>.] - ETA: 0s - loss: 0.2072 - sparse_categorical_accuracy: 0.9740WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 3ms/step - loss: 0.2070 - sparse_categorical_accuracy: 0.9740 - val_loss: 7.6991 - val_sparse_categorical_accuracy: 0.2051\n",
      "Epoch 67/10000\n",
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      "Epoch 67: saving model to training_1\\cp.ckpt\n",
      "915/932 [============================>.] - ETA: 0s - loss: 0.2042 - sparse_categorical_accuracy: 0.9747WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 3ms/step - loss: 0.2042 - sparse_categorical_accuracy: 0.9747 - val_loss: 7.7359 - val_sparse_categorical_accuracy: 0.2070\n",
      "Epoch 68/10000\n",
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      "Epoch 68: saving model to training_1\\cp.ckpt\n",
      "916/932 [============================>.] - ETA: 0s - loss: 0.2017 - sparse_categorical_accuracy: 0.9753WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 3ms/step - loss: 0.2019 - sparse_categorical_accuracy: 0.9753 - val_loss: 7.7734 - val_sparse_categorical_accuracy: 0.2081\n",
      "Epoch 69/10000\n",
      "  1/932 [..............................] - ETA: 3s - loss: 0.0965 - sparse_categorical_accuracy: 1.0000\n",
      "Epoch 69: saving model to training_1\\cp.ckpt\n",
      " 83/932 [=>............................] - ETA: 2s - loss: 0.1841 - sparse_categorical_accuracy: 0.9797\n",
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      "Epoch 69: saving model to training_1\\cp.ckpt\n",
      "482/932 [==============>...............] - ETA: 1s - loss: 0.2011 - sparse_categorical_accuracy: 0.9756\n",
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      "Epoch 69: saving model to training_1\\cp.ckpt\n",
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      "879/932 [===========================>..] - ETA: 0s - loss: 0.1982 - sparse_categorical_accuracy: 0.9759\n",
      "Epoch 69: saving model to training_1\\cp.ckpt\n",
      "929/932 [============================>.] - ETA: 0s - loss: 0.1992 - sparse_categorical_accuracy: 0.9758WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 4s 4ms/step - loss: 0.1992 - sparse_categorical_accuracy: 0.9758 - val_loss: 7.8150 - val_sparse_categorical_accuracy: 0.2048\n",
      "Epoch 70/10000\n",
      " 32/932 [>.............................] - ETA: 2s - loss: 0.2078 - sparse_categorical_accuracy: 0.9707\n",
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      "Epoch 70: saving model to training_1\\cp.ckpt\n",
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      "Epoch 70: saving model to training_1\\cp.ckpt\n",
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      "Epoch 70: saving model to training_1\\cp.ckpt\n",
      "668/932 [====================>.........] - ETA: 0s - loss: 0.1934 - sparse_categorical_accuracy: 0.9761\n",
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      "Epoch 70: saving model to training_1\\cp.ckpt\n",
      "909/932 [============================>.] - ETA: 0s - loss: 0.1967 - sparse_categorical_accuracy: 0.9752\n",
      "Epoch 70: saving model to training_1\\cp.ckpt\n",
      "916/932 [============================>.] - ETA: 0s - loss: 0.1970 - sparse_categorical_accuracy: 0.9752WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.1966 - sparse_categorical_accuracy: 0.9755 - val_loss: 7.8575 - val_sparse_categorical_accuracy: 0.2091\n",
      "Epoch 71/10000\n",
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      "Epoch 71: saving model to training_1\\cp.ckpt\n",
      "371/932 [==========>...................] - ETA: 1s - loss: 0.1866 - sparse_categorical_accuracy: 0.9786\n",
      "Epoch 71: saving model to training_1\\cp.ckpt\n",
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      "Epoch 71: saving model to training_1\\cp.ckpt\n",
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      "Epoch 71: saving model to training_1\\cp.ckpt\n",
      "693/932 [=====================>........] - ETA: 0s - loss: 0.1925 - sparse_categorical_accuracy: 0.9784\n",
      "Epoch 71: saving model to training_1\\cp.ckpt\n",
      "773/932 [=======================>......] - ETA: 0s - loss: 0.1907 - sparse_categorical_accuracy: 0.9785\n",
      "Epoch 71: saving model to training_1\\cp.ckpt\n",
      "851/932 [==========================>...] - ETA: 0s - loss: 0.1925 - sparse_categorical_accuracy: 0.9781\n",
      "Epoch 71: saving model to training_1\\cp.ckpt\n",
      "931/932 [============================>.] - ETA: 0s - loss: 0.1939 - sparse_categorical_accuracy: 0.9774WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 4s 4ms/step - loss: 0.1939 - sparse_categorical_accuracy: 0.9774 - val_loss: 7.8931 - val_sparse_categorical_accuracy: 0.2070\n",
      "Epoch 72/10000\n",
      "  1/932 [..............................] - ETA: 3s - loss: 0.1648 - sparse_categorical_accuracy: 1.0000\n",
      "Epoch 72: saving model to training_1\\cp.ckpt\n",
      " 79/932 [=>............................] - ETA: 2s - loss: 0.1872 - sparse_categorical_accuracy: 0.9818\n",
      "Epoch 72: saving model to training_1\\cp.ckpt\n",
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      "Epoch 72: saving model to training_1\\cp.ckpt\n",
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      "Epoch 72: saving model to training_1\\cp.ckpt\n",
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      "Epoch 72: saving model to training_1\\cp.ckpt\n",
      "400/932 [===========>..................] - ETA: 1s - loss: 0.1902 - sparse_categorical_accuracy: 0.9772\n",
      "Epoch 72: saving model to training_1\\cp.ckpt\n",
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      "Epoch 72: saving model to training_1\\cp.ckpt\n",
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      "Epoch 72: saving model to training_1\\cp.ckpt\n",
      "925/932 [============================>.] - ETA: 0s - loss: 0.1914 - sparse_categorical_accuracy: 0.9778WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 4s 4ms/step - loss: 0.1914 - sparse_categorical_accuracy: 0.9778 - val_loss: 7.9317 - val_sparse_categorical_accuracy: 0.2056\n",
      "Epoch 73/10000\n",
      " 34/932 [>.............................] - ETA: 2s - loss: 0.1632 - sparse_categorical_accuracy: 0.9743\n",
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      "Epoch 73: saving model to training_1\\cp.ckpt\n",
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      "Epoch 73: saving model to training_1\\cp.ckpt\n",
      "920/932 [============================>.] - ETA: 0s - loss: 0.1876 - sparse_categorical_accuracy: 0.9780WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 4s 4ms/step - loss: 0.1888 - sparse_categorical_accuracy: 0.9778 - val_loss: 7.9745 - val_sparse_categorical_accuracy: 0.2067\n",
      "Epoch 74/10000\n",
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      "Epoch 74: saving model to training_1\\cp.ckpt\n",
      "919/932 [============================>.] - ETA: 0s - loss: 0.1866 - sparse_categorical_accuracy: 0.9782WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 4s 4ms/step - loss: 0.1863 - sparse_categorical_accuracy: 0.9784 - val_loss: 8.0130 - val_sparse_categorical_accuracy: 0.2051\n",
      "Epoch 75/10000\n",
      "  1/932 [..............................] - ETA: 4s - loss: 0.1551 - sparse_categorical_accuracy: 1.0000\n",
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      "Epoch 75: saving model to training_1\\cp.ckpt\n",
      "930/932 [============================>.] - ETA: 0s - loss: 0.1841 - sparse_categorical_accuracy: 0.9787WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 4s 4ms/step - loss: 0.1840 - sparse_categorical_accuracy: 0.9787 - val_loss: 8.0570 - val_sparse_categorical_accuracy: 0.2040\n",
      "Epoch 76/10000\n",
      " 34/932 [>.............................] - ETA: 2s - loss: 0.1674 - sparse_categorical_accuracy: 0.9835\n",
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      "Epoch 76: saving model to training_1\\cp.ckpt\n",
      "912/932 [============================>.] - ETA: 0s - loss: 0.1812 - sparse_categorical_accuracy: 0.9784\n",
      "Epoch 76: saving model to training_1\\cp.ckpt\n",
      "924/932 [============================>.] - ETA: 0s - loss: 0.1813 - sparse_categorical_accuracy: 0.9787WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 4s 4ms/step - loss: 0.1815 - sparse_categorical_accuracy: 0.9787 - val_loss: 8.0942 - val_sparse_categorical_accuracy: 0.2059\n",
      "Epoch 77/10000\n",
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      "Epoch 77: saving model to training_1\\cp.ckpt\n",
      "922/932 [============================>.] - ETA: 0s - loss: 0.1786 - sparse_categorical_accuracy: 0.9801WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 4s 4ms/step - loss: 0.1788 - sparse_categorical_accuracy: 0.9800 - val_loss: 8.1382 - val_sparse_categorical_accuracy: 0.2059\n",
      "Epoch 78/10000\n",
      " 17/932 [..............................] - ETA: 2s - loss: 0.1609 - sparse_categorical_accuracy: 0.9816\n",
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      "Epoch 78: saving model to training_1\\cp.ckpt\n",
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      "Epoch 78: saving model to training_1\\cp.ckpt\n",
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      "Epoch 78: saving model to training_1\\cp.ckpt\n",
      "807/932 [========================>.....] - ETA: 0s - loss: 0.1744 - sparse_categorical_accuracy: 0.9805\n",
      "Epoch 78: saving model to training_1\\cp.ckpt\n",
      "887/932 [===========================>..] - ETA: 0s - loss: 0.1772 - sparse_categorical_accuracy: 0.9796\n",
      "Epoch 78: saving model to training_1\\cp.ckpt\n",
      "917/932 [============================>.] - ETA: 0s - loss: 0.1769 - sparse_categorical_accuracy: 0.9798WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 4s 4ms/step - loss: 0.1767 - sparse_categorical_accuracy: 0.9798 - val_loss: 8.1768 - val_sparse_categorical_accuracy: 0.2043\n",
      "Epoch 79/10000\n",
      " 38/932 [>.............................] - ETA: 2s - loss: 0.1670 - sparse_categorical_accuracy: 0.9770\n",
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      "Epoch 79: saving model to training_1\\cp.ckpt\n",
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      "Epoch 79: saving model to training_1\\cp.ckpt\n",
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      "Epoch 79: saving model to training_1\\cp.ckpt\n",
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      "Epoch 79: saving model to training_1\\cp.ckpt\n",
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      "Epoch 79: saving model to training_1\\cp.ckpt\n",
      "676/932 [====================>.........] - ETA: 0s - loss: 0.1705 - sparse_categorical_accuracy: 0.9815\n",
      "Epoch 79: saving model to training_1\\cp.ckpt\n",
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      "Epoch 79: saving model to training_1\\cp.ckpt\n",
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      "Epoch 79: saving model to training_1\\cp.ckpt\n",
      "916/932 [============================>.] - ETA: 0s - loss: 0.1741 - sparse_categorical_accuracy: 0.9808\n",
      "Epoch 79: saving model to training_1\\cp.ckpt\n",
      "928/932 [============================>.] - ETA: 0s - loss: 0.1744 - sparse_categorical_accuracy: 0.9806WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 4s 4ms/step - loss: 0.1746 - sparse_categorical_accuracy: 0.9805 - val_loss: 8.2170 - val_sparse_categorical_accuracy: 0.2054\n",
      "Epoch 80/10000\n",
      " 69/932 [=>............................] - ETA: 2s - loss: 0.1757 - sparse_categorical_accuracy: 0.9801\n",
      "Epoch 80: saving model to training_1\\cp.ckpt\n",
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      "Epoch 80: saving model to training_1\\cp.ckpt\n",
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      "Epoch 80: saving model to training_1\\cp.ckpt\n",
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      "Epoch 80: saving model to training_1\\cp.ckpt\n",
      "383/932 [===========>..................] - ETA: 1s - loss: 0.1686 - sparse_categorical_accuracy: 0.9825\n",
      "Epoch 80: saving model to training_1\\cp.ckpt\n",
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      "Epoch 80: saving model to training_1\\cp.ckpt\n",
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      "Epoch 80: saving model to training_1\\cp.ckpt\n",
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      "Epoch 80: saving model to training_1\\cp.ckpt\n",
      "703/932 [=====================>........] - ETA: 0s - loss: 0.1705 - sparse_categorical_accuracy: 0.9813\n",
      "Epoch 80: saving model to training_1\\cp.ckpt\n",
      "783/932 [========================>.....] - ETA: 0s - loss: 0.1710 - sparse_categorical_accuracy: 0.9813\n",
      "Epoch 80: saving model to training_1\\cp.ckpt\n",
      "867/932 [==========================>...] - ETA: 0s - loss: 0.1716 - sparse_categorical_accuracy: 0.9808\n",
      "Epoch 80: saving model to training_1\\cp.ckpt\n",
      "925/932 [============================>.] - ETA: 0s - loss: 0.1719 - sparse_categorical_accuracy: 0.9806WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 4s 4ms/step - loss: 0.1718 - sparse_categorical_accuracy: 0.9807 - val_loss: 8.2617 - val_sparse_categorical_accuracy: 0.2054\n",
      "Epoch 81/10000\n",
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      "Epoch 81: saving model to training_1\\cp.ckpt\n",
      "924/932 [============================>.] - ETA: 0s - loss: 0.1699 - sparse_categorical_accuracy: 0.9809WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 4s 4ms/step - loss: 0.1698 - sparse_categorical_accuracy: 0.9809 - val_loss: 8.2977 - val_sparse_categorical_accuracy: 0.2027\n",
      "Epoch 82/10000\n",
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      "919/932 [============================>.] - ETA: 0s - loss: 0.1665 - sparse_categorical_accuracy: 0.9813\n",
      "Epoch 82: saving model to training_1\\cp.ckpt\n",
      "932/932 [==============================] - ETA: 0s - loss: 0.1673 - sparse_categorical_accuracy: 0.9811WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 4s 4ms/step - loss: 0.1673 - sparse_categorical_accuracy: 0.9811 - val_loss: 8.3404 - val_sparse_categorical_accuracy: 0.2021\n",
      "Epoch 83/10000\n",
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      "Epoch 83: saving model to training_1\\cp.ckpt\n",
      "875/932 [===========================>..] - ETA: 0s - loss: 0.1648 - sparse_categorical_accuracy: 0.9820\n",
      "Epoch 83: saving model to training_1\\cp.ckpt\n",
      "925/932 [============================>.] - ETA: 0s - loss: 0.1655 - sparse_categorical_accuracy: 0.9820WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 4s 4ms/step - loss: 0.1652 - sparse_categorical_accuracy: 0.9820 - val_loss: 8.3770 - val_sparse_categorical_accuracy: 0.2048\n",
      "Epoch 84/10000\n",
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      "923/932 [============================>.] - ETA: 0s - loss: 0.1632 - sparse_categorical_accuracy: 0.9822WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 4s 4ms/step - loss: 0.1630 - sparse_categorical_accuracy: 0.9823 - val_loss: 8.4200 - val_sparse_categorical_accuracy: 0.2043\n",
      "Epoch 85/10000\n",
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      "921/932 [============================>.] - ETA: 0s - loss: 0.1608 - sparse_categorical_accuracy: 0.9823WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 4s 4ms/step - loss: 0.1607 - sparse_categorical_accuracy: 0.9823 - val_loss: 8.4654 - val_sparse_categorical_accuracy: 0.2038\n",
      "Epoch 86/10000\n",
      "  1/932 [..............................] - ETA: 4s - loss: 0.0844 - sparse_categorical_accuracy: 1.0000\n",
      "Epoch 86: saving model to training_1\\cp.ckpt\n",
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      "870/932 [===========================>..] - ETA: 0s - loss: 0.1577 - sparse_categorical_accuracy: 0.9834\n",
      "Epoch 86: saving model to training_1\\cp.ckpt\n",
      "930/932 [============================>.] - ETA: 0s - loss: 0.1586 - sparse_categorical_accuracy: 0.9831WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 4s 4ms/step - loss: 0.1587 - sparse_categorical_accuracy: 0.9831 - val_loss: 8.5056 - val_sparse_categorical_accuracy: 0.2038\n",
      "Epoch 87/10000\n",
      " 30/932 [..............................] - ETA: 3s - loss: 0.1133 - sparse_categorical_accuracy: 0.9937\n",
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      "Epoch 87: saving model to training_1\\cp.ckpt\n",
      "659/932 [====================>.........] - ETA: 1s - loss: 0.1547 - sparse_categorical_accuracy: 0.9839\n",
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      "820/932 [=========================>....] - ETA: 0s - loss: 0.1554 - sparse_categorical_accuracy: 0.9841\n",
      "Epoch 87: saving model to training_1\\cp.ckpt\n",
      "911/932 [============================>.] - ETA: 0s - loss: 0.1570 - sparse_categorical_accuracy: 0.9837\n",
      "Epoch 87: saving model to training_1\\cp.ckpt\n",
      "918/932 [============================>.] - ETA: 0s - loss: 0.1568 - sparse_categorical_accuracy: 0.9837WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 4s 4ms/step - loss: 0.1564 - sparse_categorical_accuracy: 0.9838 - val_loss: 8.5469 - val_sparse_categorical_accuracy: 0.2038\n",
      "Epoch 88/10000\n",
      " 44/932 [>.............................] - ETA: 3s - loss: 0.1688 - sparse_categorical_accuracy: 0.9744\n",
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      "Epoch 88: saving model to training_1\\cp.ckpt\n",
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      "Epoch 88: saving model to training_1\\cp.ckpt\n",
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      "Epoch 88: saving model to training_1\\cp.ckpt\n",
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      "Epoch 88: saving model to training_1\\cp.ckpt\n",
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      "Epoch 88: saving model to training_1\\cp.ckpt\n",
      "766/932 [=======================>......] - ETA: 0s - loss: 0.1521 - sparse_categorical_accuracy: 0.9849\n",
      "Epoch 88: saving model to training_1\\cp.ckpt\n",
      "848/932 [==========================>...] - ETA: 0s - loss: 0.1533 - sparse_categorical_accuracy: 0.9840\n",
      "Epoch 88: saving model to training_1\\cp.ckpt\n",
      "925/932 [============================>.] - ETA: 0s - loss: 0.1541 - sparse_categorical_accuracy: 0.9836WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 4s 4ms/step - loss: 0.1541 - sparse_categorical_accuracy: 0.9836 - val_loss: 8.5859 - val_sparse_categorical_accuracy: 0.2030\n",
      "Epoch 89/10000\n",
      "  1/932 [..............................] - ETA: 3s - loss: 0.1855 - sparse_categorical_accuracy: 1.0000\n",
      "Epoch 89: saving model to training_1\\cp.ckpt\n",
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      "Epoch 89: saving model to training_1\\cp.ckpt\n",
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      "Epoch 89: saving model to training_1\\cp.ckpt\n",
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      "Epoch 89: saving model to training_1\\cp.ckpt\n",
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      "Epoch 89: saving model to training_1\\cp.ckpt\n",
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      "Epoch 89: saving model to training_1\\cp.ckpt\n",
      "930/932 [============================>.] - ETA: 0s - loss: 0.1524 - sparse_categorical_accuracy: 0.9831WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.1523 - sparse_categorical_accuracy: 0.9832 - val_loss: 8.6269 - val_sparse_categorical_accuracy: 0.2021\n",
      "Epoch 90/10000\n",
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      "Epoch 90: saving model to training_1\\cp.ckpt\n",
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      "Epoch 90: saving model to training_1\\cp.ckpt\n",
      "918/932 [============================>.] - ETA: 0s - loss: 0.1493 - sparse_categorical_accuracy: 0.9838WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.1498 - sparse_categorical_accuracy: 0.9838 - val_loss: 8.6734 - val_sparse_categorical_accuracy: 0.2021\n",
      "Epoch 91/10000\n",
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      "Epoch 91: saving model to training_1\\cp.ckpt\n",
      "922/932 [============================>.] - ETA: 0s - loss: 0.1477 - sparse_categorical_accuracy: 0.9839WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.1479 - sparse_categorical_accuracy: 0.9839 - val_loss: 8.7106 - val_sparse_categorical_accuracy: 0.2019\n",
      "Epoch 92/10000\n",
      "  1/932 [..............................] - ETA: 3s - loss: 0.0941 - sparse_categorical_accuracy: 1.0000\n",
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      "Epoch 92: saving model to training_1\\cp.ckpt\n",
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      "Epoch 92: saving model to training_1\\cp.ckpt\n",
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      "Epoch 92: saving model to training_1\\cp.ckpt\n",
      "929/932 [============================>.] - ETA: 0s - loss: 0.1460 - sparse_categorical_accuracy: 0.9849WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.1458 - sparse_categorical_accuracy: 0.9849 - val_loss: 8.7543 - val_sparse_categorical_accuracy: 0.2021\n",
      "Epoch 93/10000\n",
      " 39/932 [>.............................] - ETA: 2s - loss: 0.1445 - sparse_categorical_accuracy: 0.9856\n",
      "Epoch 93: saving model to training_1\\cp.ckpt\n",
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      "Epoch 93: saving model to training_1\\cp.ckpt\n",
      "915/932 [============================>.] - ETA: 0s - loss: 0.1438 - sparse_categorical_accuracy: 0.9854\n",
      "Epoch 93: saving model to training_1\\cp.ckpt\n",
      "922/932 [============================>.] - ETA: 0s - loss: 0.1441 - sparse_categorical_accuracy: 0.9853WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.1438 - sparse_categorical_accuracy: 0.9854 - val_loss: 8.7968 - val_sparse_categorical_accuracy: 0.2008\n",
      "Epoch 94/10000\n",
      " 58/932 [>.............................] - ETA: 2s - loss: 0.1162 - sparse_categorical_accuracy: 0.9925\n",
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      "Epoch 94: saving model to training_1\\cp.ckpt\n",
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      "Epoch 94: saving model to training_1\\cp.ckpt\n",
      "849/932 [==========================>...] - ETA: 0s - loss: 0.1412 - sparse_categorical_accuracy: 0.9862\n",
      "Epoch 94: saving model to training_1\\cp.ckpt\n",
      "922/932 [============================>.] - ETA: 0s - loss: 0.1420 - sparse_categorical_accuracy: 0.9857WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 3ms/step - loss: 0.1420 - sparse_categorical_accuracy: 0.9857 - val_loss: 8.8365 - val_sparse_categorical_accuracy: 0.2024\n",
      "Epoch 95/10000\n",
      "  1/932 [..............................] - ETA: 3s - loss: 0.1533 - sparse_categorical_accuracy: 1.0000\n",
      "Epoch 95: saving model to training_1\\cp.ckpt\n",
      " 88/932 [=>............................] - ETA: 2s - loss: 0.1445 - sparse_categorical_accuracy: 0.9872\n",
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      "Epoch 95: saving model to training_1\\cp.ckpt\n",
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      "Epoch 95: saving model to training_1\\cp.ckpt\n",
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      "Epoch 95: saving model to training_1\\cp.ckpt\n",
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      "Epoch 95: saving model to training_1\\cp.ckpt\n",
      "891/932 [===========================>..] - ETA: 0s - loss: 0.1403 - sparse_categorical_accuracy: 0.9855\n",
      "Epoch 95: saving model to training_1\\cp.ckpt\n",
      "916/932 [============================>.] - ETA: 0s - loss: 0.1401 - sparse_categorical_accuracy: 0.9857WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.1399 - sparse_categorical_accuracy: 0.9857 - val_loss: 8.8798 - val_sparse_categorical_accuracy: 0.2027\n",
      "Epoch 96/10000\n",
      " 39/932 [>.............................] - ETA: 2s - loss: 0.1390 - sparse_categorical_accuracy: 0.9872\n",
      "Epoch 96: saving model to training_1\\cp.ckpt\n",
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      "Epoch 96: saving model to training_1\\cp.ckpt\n",
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      "Epoch 96: saving model to training_1\\cp.ckpt\n",
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      "Epoch 96: saving model to training_1\\cp.ckpt\n",
      "358/932 [==========>...................] - ETA: 1s - loss: 0.1343 - sparse_categorical_accuracy: 0.9876\n",
      "Epoch 96: saving model to training_1\\cp.ckpt\n",
      "436/932 [=============>................] - ETA: 1s - loss: 0.1346 - sparse_categorical_accuracy: 0.9875\n",
      "Epoch 96: saving model to training_1\\cp.ckpt\n",
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      "Epoch 96: saving model to training_1\\cp.ckpt\n",
      "603/932 [==================>...........] - ETA: 1s - loss: 0.1357 - sparse_categorical_accuracy: 0.9871\n",
      "Epoch 96: saving model to training_1\\cp.ckpt\n",
      "666/932 [====================>.........] - ETA: 0s - loss: 0.1360 - sparse_categorical_accuracy: 0.9870\n",
      "Epoch 96: saving model to training_1\\cp.ckpt\n",
      "756/932 [=======================>......] - ETA: 0s - loss: 0.1365 - sparse_categorical_accuracy: 0.9869\n",
      "Epoch 96: saving model to training_1\\cp.ckpt\n",
      "838/932 [=========================>....] - ETA: 0s - loss: 0.1377 - sparse_categorical_accuracy: 0.9864\n",
      "Epoch 96: saving model to training_1\\cp.ckpt\n",
      "917/932 [============================>.] - ETA: 0s - loss: 0.1380 - sparse_categorical_accuracy: 0.9862\n",
      "Epoch 96: saving model to training_1\\cp.ckpt\n",
      "925/932 [============================>.] - ETA: 0s - loss: 0.1380 - sparse_categorical_accuracy: 0.9861WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.1378 - sparse_categorical_accuracy: 0.9862 - val_loss: 8.9193 - val_sparse_categorical_accuracy: 0.2011\n",
      "Epoch 97/10000\n",
      " 55/932 [>.............................] - ETA: 2s - loss: 0.1244 - sparse_categorical_accuracy: 0.9875\n",
      "Epoch 97: saving model to training_1\\cp.ckpt\n",
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      "Epoch 97: saving model to training_1\\cp.ckpt\n",
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      "Epoch 97: saving model to training_1\\cp.ckpt\n",
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      "Epoch 97: saving model to training_1\\cp.ckpt\n",
      "388/932 [===========>..................] - ETA: 1s - loss: 0.1295 - sparse_categorical_accuracy: 0.9881\n",
      "Epoch 97: saving model to training_1\\cp.ckpt\n",
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      "Epoch 97: saving model to training_1\\cp.ckpt\n",
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      "Epoch 97: saving model to training_1\\cp.ckpt\n",
      "627/932 [===================>..........] - ETA: 0s - loss: 0.1357 - sparse_categorical_accuracy: 0.9867\n",
      "Epoch 97: saving model to training_1\\cp.ckpt\n",
      "709/932 [=====================>........] - ETA: 0s - loss: 0.1366 - sparse_categorical_accuracy: 0.9861\n",
      "Epoch 97: saving model to training_1\\cp.ckpt\n",
      "790/932 [========================>.....] - ETA: 0s - loss: 0.1372 - sparse_categorical_accuracy: 0.9866\n",
      "Epoch 97: saving model to training_1\\cp.ckpt\n",
      "854/932 [==========================>...] - ETA: 0s - loss: 0.1368 - sparse_categorical_accuracy: 0.9865\n",
      "Epoch 97: saving model to training_1\\cp.ckpt\n",
      "928/932 [============================>.] - ETA: 0s - loss: 0.1360 - sparse_categorical_accuracy: 0.9864WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 3ms/step - loss: 0.1360 - sparse_categorical_accuracy: 0.9864 - val_loss: 8.9665 - val_sparse_categorical_accuracy: 0.1987\n",
      "Epoch 98/10000\n",
      " 19/932 [..............................] - ETA: 2s - loss: 0.1376 - sparse_categorical_accuracy: 0.9836\n",
      "Epoch 98: saving model to training_1\\cp.ckpt\n",
      " 81/932 [=>............................] - ETA: 2s - loss: 0.1102 - sparse_categorical_accuracy: 0.9938\n",
      "Epoch 98: saving model to training_1\\cp.ckpt\n",
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      "Epoch 98: saving model to training_1\\cp.ckpt\n",
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      "Epoch 98: saving model to training_1\\cp.ckpt\n",
      "931/932 [============================>.] - ETA: 0s - loss: 0.1338 - sparse_categorical_accuracy: 0.9860WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.1338 - sparse_categorical_accuracy: 0.9860 - val_loss: 9.0108 - val_sparse_categorical_accuracy: 0.2011\n",
      "Epoch 99/10000\n",
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      "Epoch 99: saving model to training_1\\cp.ckpt\n",
      "924/932 [============================>.] - ETA: 0s - loss: 0.1318 - sparse_categorical_accuracy: 0.9869\n",
      "Epoch 99: saving model to training_1\\cp.ckpt\n",
      "931/932 [============================>.] - ETA: 0s - loss: 0.1321 - sparse_categorical_accuracy: 0.9870WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.1321 - sparse_categorical_accuracy: 0.9870 - val_loss: 9.0525 - val_sparse_categorical_accuracy: 0.2016\n",
      "Epoch 100/10000\n",
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      "Epoch 100: saving model to training_1\\cp.ckpt\n",
      "932/932 [==============================] - ETA: 0s - loss: 0.1302 - sparse_categorical_accuracy: 0.9869WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.1302 - sparse_categorical_accuracy: 0.9869 - val_loss: 9.0954 - val_sparse_categorical_accuracy: 0.2003\n",
      "Epoch 101/10000\n",
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      "896/932 [===========================>..] - ETA: 0s - loss: 0.1279 - sparse_categorical_accuracy: 0.9873\n",
      "Epoch 101: saving model to training_1\\cp.ckpt\n",
      "923/932 [============================>.] - ETA: 0s - loss: 0.1282 - sparse_categorical_accuracy: 0.9871WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.1282 - sparse_categorical_accuracy: 0.9871 - val_loss: 9.1406 - val_sparse_categorical_accuracy: 0.1995\n",
      "Epoch 102/10000\n",
      " 38/932 [>.............................] - ETA: 2s - loss: 0.1045 - sparse_categorical_accuracy: 0.9852\n",
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      "926/932 [============================>.] - ETA: 0s - loss: 0.1264 - sparse_categorical_accuracy: 0.9872\n",
      "Epoch 102: saving model to training_1\\cp.ckpt\n",
      "932/932 [==============================] - ETA: 0s - loss: 0.1265 - sparse_categorical_accuracy: 0.9872WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.1265 - sparse_categorical_accuracy: 0.9872 - val_loss: 9.1803 - val_sparse_categorical_accuracy: 0.2027\n",
      "Epoch 103/10000\n",
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      "Epoch 103: saving model to training_1\\cp.ckpt\n",
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      "879/932 [===========================>..] - ETA: 0s - loss: 0.1248 - sparse_categorical_accuracy: 0.9872\n",
      "Epoch 103: saving model to training_1\\cp.ckpt\n",
      "922/932 [============================>.] - ETA: 0s - loss: 0.1245 - sparse_categorical_accuracy: 0.9875WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.1246 - sparse_categorical_accuracy: 0.9876 - val_loss: 9.2226 - val_sparse_categorical_accuracy: 0.2003\n",
      "Epoch 104/10000\n",
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      "Epoch 104: saving model to training_1\\cp.ckpt\n",
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      "Epoch 104: saving model to training_1\\cp.ckpt\n",
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      "Epoch 104: saving model to training_1\\cp.ckpt\n",
      "899/932 [===========================>..] - ETA: 0s - loss: 0.1217 - sparse_categorical_accuracy: 0.9881\n",
      "Epoch 104: saving model to training_1\\cp.ckpt\n",
      "926/932 [============================>.] - ETA: 0s - loss: 0.1227 - sparse_categorical_accuracy: 0.9880WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.1228 - sparse_categorical_accuracy: 0.9878 - val_loss: 9.2656 - val_sparse_categorical_accuracy: 0.1995\n",
      "Epoch 105/10000\n",
      " 55/932 [>.............................] - ETA: 2s - loss: 0.1130 - sparse_categorical_accuracy: 0.9898\n",
      "Epoch 105: saving model to training_1\\cp.ckpt\n",
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      "Epoch 105: saving model to training_1\\cp.ckpt\n",
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      "Epoch 105: saving model to training_1\\cp.ckpt\n",
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      "Epoch 105: saving model to training_1\\cp.ckpt\n",
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      "Epoch 105: saving model to training_1\\cp.ckpt\n",
      "531/932 [================>.............] - ETA: 1s - loss: 0.1170 - sparse_categorical_accuracy: 0.9898\n",
      "Epoch 105: saving model to training_1\\cp.ckpt\n",
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      "Epoch 105: saving model to training_1\\cp.ckpt\n",
      "680/932 [====================>.........] - ETA: 0s - loss: 0.1174 - sparse_categorical_accuracy: 0.9894\n",
      "Epoch 105: saving model to training_1\\cp.ckpt\n",
      "759/932 [=======================>......] - ETA: 0s - loss: 0.1187 - sparse_categorical_accuracy: 0.9890\n",
      "Epoch 105: saving model to training_1\\cp.ckpt\n",
      "849/932 [==========================>...] - ETA: 0s - loss: 0.1211 - sparse_categorical_accuracy: 0.9879\n",
      "Epoch 105: saving model to training_1\\cp.ckpt\n",
      "928/932 [============================>.] - ETA: 0s - loss: 0.1212 - sparse_categorical_accuracy: 0.9877WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.1211 - sparse_categorical_accuracy: 0.9877 - val_loss: 9.3107 - val_sparse_categorical_accuracy: 0.1997\n",
      "Epoch 106/10000\n",
      "  1/932 [..............................] - ETA: 3s - loss: 0.0948 - sparse_categorical_accuracy: 1.0000\n",
      "Epoch 106: saving model to training_1\\cp.ckpt\n",
      " 80/932 [=>............................] - ETA: 2s - loss: 0.1044 - sparse_categorical_accuracy: 0.9906\n",
      "Epoch 106: saving model to training_1\\cp.ckpt\n",
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      "Epoch 106: saving model to training_1\\cp.ckpt\n",
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      "Epoch 106: saving model to training_1\\cp.ckpt\n",
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      "Epoch 106: saving model to training_1\\cp.ckpt\n",
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      "Epoch 106: saving model to training_1\\cp.ckpt\n",
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      "Epoch 106: saving model to training_1\\cp.ckpt\n",
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      "Epoch 106: saving model to training_1\\cp.ckpt\n",
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      "Epoch 106: saving model to training_1\\cp.ckpt\n",
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      "Epoch 106: saving model to training_1\\cp.ckpt\n",
      "878/932 [===========================>..] - ETA: 0s - loss: 0.1195 - sparse_categorical_accuracy: 0.9880\n",
      "Epoch 106: saving model to training_1\\cp.ckpt\n",
      "920/932 [============================>.] - ETA: 0s - loss: 0.1194 - sparse_categorical_accuracy: 0.9881WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.1194 - sparse_categorical_accuracy: 0.9881 - val_loss: 9.3590 - val_sparse_categorical_accuracy: 0.1992\n",
      "Epoch 107/10000\n",
      " 19/932 [..............................] - ETA: 2s - loss: 0.1068 - sparse_categorical_accuracy: 0.9901\n",
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      "Epoch 107: saving model to training_1\\cp.ckpt\n",
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      "584/932 [=================>............] - ETA: 1s - loss: 0.1142 - sparse_categorical_accuracy: 0.9896\n",
      "Epoch 107: saving model to training_1\\cp.ckpt\n",
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      "738/932 [======================>.......] - ETA: 0s - loss: 0.1182 - sparse_categorical_accuracy: 0.9886\n",
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      "Epoch 107: saving model to training_1\\cp.ckpt\n",
      "904/932 [============================>.] - ETA: 0s - loss: 0.1176 - sparse_categorical_accuracy: 0.9888\n",
      "Epoch 107: saving model to training_1\\cp.ckpt\n",
      "931/932 [============================>.] - ETA: 0s - loss: 0.1176 - sparse_categorical_accuracy: 0.9889WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.1177 - sparse_categorical_accuracy: 0.9889 - val_loss: 9.3945 - val_sparse_categorical_accuracy: 0.1995\n",
      "Epoch 108/10000\n",
      " 54/932 [>.............................] - ETA: 2s - loss: 0.1123 - sparse_categorical_accuracy: 0.9896\n",
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      "531/932 [================>.............] - ETA: 1s - loss: 0.1102 - sparse_categorical_accuracy: 0.9898\n",
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      "859/932 [==========================>...] - ETA: 0s - loss: 0.1149 - sparse_categorical_accuracy: 0.9891\n",
      "Epoch 108: saving model to training_1\\cp.ckpt\n",
      "932/932 [==============================] - ETA: 0s - loss: 0.1160 - sparse_categorical_accuracy: 0.9887WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.1160 - sparse_categorical_accuracy: 0.9887 - val_loss: 9.4410 - val_sparse_categorical_accuracy: 0.2003\n",
      "Epoch 109/10000\n",
      "  1/932 [..............................] - ETA: 3s - loss: 0.1786 - sparse_categorical_accuracy: 1.0000\n",
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      "Epoch 109: saving model to training_1\\cp.ckpt\n",
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      "Epoch 109: saving model to training_1\\cp.ckpt\n",
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      "881/932 [===========================>..] - ETA: 0s - loss: 0.1137 - sparse_categorical_accuracy: 0.9891\n",
      "Epoch 109: saving model to training_1\\cp.ckpt\n",
      "924/932 [============================>.] - ETA: 0s - loss: 0.1143 - sparse_categorical_accuracy: 0.9890WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.1141 - sparse_categorical_accuracy: 0.9890 - val_loss: 9.4850 - val_sparse_categorical_accuracy: 0.2005\n",
      "Epoch 110/10000\n",
      " 19/932 [..............................] - ETA: 2s - loss: 0.0979 - sparse_categorical_accuracy: 0.9967\n",
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      "829/932 [=========================>....] - ETA: 0s - loss: 0.1115 - sparse_categorical_accuracy: 0.9897\n",
      "Epoch 110: saving model to training_1\\cp.ckpt\n",
      "905/932 [============================>.] - ETA: 0s - loss: 0.1126 - sparse_categorical_accuracy: 0.9892\n",
      "Epoch 110: saving model to training_1\\cp.ckpt\n",
      "916/932 [============================>.] - ETA: 0s - loss: 0.1124 - sparse_categorical_accuracy: 0.9893WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.1126 - sparse_categorical_accuracy: 0.9893 - val_loss: 9.5353 - val_sparse_categorical_accuracy: 0.2008\n",
      "Epoch 111/10000\n",
      " 56/932 [>.............................] - ETA: 2s - loss: 0.1041 - sparse_categorical_accuracy: 0.9911\n",
      "Epoch 111: saving model to training_1\\cp.ckpt\n",
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      "Epoch 111: saving model to training_1\\cp.ckpt\n",
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      "Epoch 111: saving model to training_1\\cp.ckpt\n",
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      "Epoch 111: saving model to training_1\\cp.ckpt\n",
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      "Epoch 111: saving model to training_1\\cp.ckpt\n",
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      "Epoch 111: saving model to training_1\\cp.ckpt\n",
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      "Epoch 111: saving model to training_1\\cp.ckpt\n",
      "695/932 [=====================>........] - ETA: 0s - loss: 0.1104 - sparse_categorical_accuracy: 0.9898\n",
      "Epoch 111: saving model to training_1\\cp.ckpt\n",
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      "Epoch 111: saving model to training_1\\cp.ckpt\n",
      "857/932 [==========================>...] - ETA: 0s - loss: 0.1115 - sparse_categorical_accuracy: 0.9894\n",
      "Epoch 111: saving model to training_1\\cp.ckpt\n",
      "919/932 [============================>.] - ETA: 0s - loss: 0.1110 - sparse_categorical_accuracy: 0.9897WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.1109 - sparse_categorical_accuracy: 0.9898 - val_loss: 9.5728 - val_sparse_categorical_accuracy: 0.1995\n",
      "Epoch 112/10000\n",
      "  1/932 [..............................] - ETA: 4s - loss: 0.0604 - sparse_categorical_accuracy: 1.0000\n",
      "Epoch 112: saving model to training_1\\cp.ckpt\n",
      " 85/932 [=>............................] - ETA: 3s - loss: 0.1035 - sparse_categorical_accuracy: 0.9912\n",
      "Epoch 112: saving model to training_1\\cp.ckpt\n",
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      "Epoch 112: saving model to training_1\\cp.ckpt\n",
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      "Epoch 112: saving model to training_1\\cp.ckpt\n",
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      "Epoch 112: saving model to training_1\\cp.ckpt\n",
      "399/932 [===========>..................] - ETA: 1s - loss: 0.1069 - sparse_categorical_accuracy: 0.9900\n",
      "Epoch 112: saving model to training_1\\cp.ckpt\n",
      "479/932 [==============>...............] - ETA: 1s - loss: 0.1062 - sparse_categorical_accuracy: 0.9896\n",
      "Epoch 112: saving model to training_1\\cp.ckpt\n",
      "563/932 [=================>............] - ETA: 1s - loss: 0.1064 - sparse_categorical_accuracy: 0.9893\n",
      "Epoch 112: saving model to training_1\\cp.ckpt\n",
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      "Epoch 112: saving model to training_1\\cp.ckpt\n",
      "726/932 [======================>.......] - ETA: 0s - loss: 0.1077 - sparse_categorical_accuracy: 0.9894\n",
      "Epoch 112: saving model to training_1\\cp.ckpt\n",
      "806/932 [========================>.....] - ETA: 0s - loss: 0.1084 - sparse_categorical_accuracy: 0.9894\n",
      "Epoch 112: saving model to training_1\\cp.ckpt\n",
      "886/932 [===========================>..] - ETA: 0s - loss: 0.1093 - sparse_categorical_accuracy: 0.9894\n",
      "Epoch 112: saving model to training_1\\cp.ckpt\n",
      "931/932 [============================>.] - ETA: 0s - loss: 0.1092 - sparse_categorical_accuracy: 0.9894WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.1092 - sparse_categorical_accuracy: 0.9894 - val_loss: 9.6167 - val_sparse_categorical_accuracy: 0.2013\n",
      "Epoch 113/10000\n",
      " 38/932 [>.............................] - ETA: 2s - loss: 0.1008 - sparse_categorical_accuracy: 0.9934\n",
      "Epoch 113: saving model to training_1\\cp.ckpt\n",
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      "Epoch 113: saving model to training_1\\cp.ckpt\n",
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      "Epoch 113: saving model to training_1\\cp.ckpt\n",
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      "Epoch 113: saving model to training_1\\cp.ckpt\n",
      "357/932 [==========>...................] - ETA: 1s - loss: 0.1013 - sparse_categorical_accuracy: 0.9909\n",
      "Epoch 113: saving model to training_1\\cp.ckpt\n",
      "436/932 [=============>................] - ETA: 1s - loss: 0.1035 - sparse_categorical_accuracy: 0.9904\n",
      "Epoch 113: saving model to training_1\\cp.ckpt\n",
      "513/932 [===============>..............] - ETA: 1s - loss: 0.1043 - sparse_categorical_accuracy: 0.9899\n",
      "Epoch 113: saving model to training_1\\cp.ckpt\n",
      "593/932 [==================>...........] - ETA: 1s - loss: 0.1054 - sparse_categorical_accuracy: 0.9899\n",
      "Epoch 113: saving model to training_1\\cp.ckpt\n",
      "672/932 [====================>.........] - ETA: 0s - loss: 0.1065 - sparse_categorical_accuracy: 0.9898\n",
      "Epoch 113: saving model to training_1\\cp.ckpt\n",
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      "Epoch 113: saving model to training_1\\cp.ckpt\n",
      "829/932 [=========================>....] - ETA: 0s - loss: 0.1078 - sparse_categorical_accuracy: 0.9894\n",
      "Epoch 113: saving model to training_1\\cp.ckpt\n",
      "912/932 [============================>.] - ETA: 0s - loss: 0.1079 - sparse_categorical_accuracy: 0.9895\n",
      "Epoch 113: saving model to training_1\\cp.ckpt\n",
      "920/932 [============================>.] - ETA: 0s - loss: 0.1077 - sparse_categorical_accuracy: 0.9896WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.1077 - sparse_categorical_accuracy: 0.9895 - val_loss: 9.6592 - val_sparse_categorical_accuracy: 0.1992\n",
      "Epoch 114/10000\n",
      " 57/932 [>.............................] - ETA: 2s - loss: 0.1072 - sparse_categorical_accuracy: 0.9890\n",
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      "Epoch 114: saving model to training_1\\cp.ckpt\n",
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      "Epoch 114: saving model to training_1\\cp.ckpt\n",
      "382/932 [===========>..................] - ETA: 1s - loss: 0.1057 - sparse_categorical_accuracy: 0.9887\n",
      "Epoch 114: saving model to training_1\\cp.ckpt\n",
      "465/932 [=============>................] - ETA: 1s - loss: 0.1077 - sparse_categorical_accuracy: 0.9887\n",
      "Epoch 114: saving model to training_1\\cp.ckpt\n",
      "545/932 [================>.............] - ETA: 1s - loss: 0.1081 - sparse_categorical_accuracy: 0.9885\n",
      "Epoch 114: saving model to training_1\\cp.ckpt\n",
      "625/932 [===================>..........] - ETA: 0s - loss: 0.1067 - sparse_categorical_accuracy: 0.9892\n",
      "Epoch 114: saving model to training_1\\cp.ckpt\n",
      "704/932 [=====================>........] - ETA: 0s - loss: 0.1065 - sparse_categorical_accuracy: 0.9894\n",
      "Epoch 114: saving model to training_1\\cp.ckpt\n",
      "784/932 [========================>.....] - ETA: 0s - loss: 0.1061 - sparse_categorical_accuracy: 0.9896\n",
      "Epoch 114: saving model to training_1\\cp.ckpt\n",
      "862/932 [==========================>...] - ETA: 0s - loss: 0.1059 - sparse_categorical_accuracy: 0.9897\n",
      "Epoch 114: saving model to training_1\\cp.ckpt\n",
      "927/932 [============================>.] - ETA: 0s - loss: 0.1060 - sparse_categorical_accuracy: 0.9898WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.1060 - sparse_categorical_accuracy: 0.9897 - val_loss: 9.7053 - val_sparse_categorical_accuracy: 0.1987\n",
      "Epoch 115/10000\n",
      "  1/932 [..............................] - ETA: 4s - loss: 0.0854 - sparse_categorical_accuracy: 1.0000\n",
      "Epoch 115: saving model to training_1\\cp.ckpt\n",
      " 93/932 [=>............................] - ETA: 3s - loss: 0.1066 - sparse_categorical_accuracy: 0.9919\n",
      "Epoch 115: saving model to training_1\\cp.ckpt\n",
      "164/932 [====>.........................] - ETA: 2s - loss: 0.0948 - sparse_categorical_accuracy: 0.9935\n",
      "Epoch 115: saving model to training_1\\cp.ckpt\n",
      "243/932 [======>.......................] - ETA: 2s - loss: 0.0963 - sparse_categorical_accuracy: 0.9925\n",
      "Epoch 115: saving model to training_1\\cp.ckpt\n",
      "327/932 [=========>....................] - ETA: 2s - loss: 0.0988 - sparse_categorical_accuracy: 0.9918\n",
      "Epoch 115: saving model to training_1\\cp.ckpt\n",
      "406/932 [============>.................] - ETA: 1s - loss: 0.0991 - sparse_categorical_accuracy: 0.9917\n",
      "Epoch 115: saving model to training_1\\cp.ckpt\n",
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      "889/932 [===========================>..] - ETA: 0s - loss: 0.1045 - sparse_categorical_accuracy: 0.9902\n",
      "Epoch 115: saving model to training_1\\cp.ckpt\n",
      "932/932 [==============================] - ETA: 0s - loss: 0.1045 - sparse_categorical_accuracy: 0.9901WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.1045 - sparse_categorical_accuracy: 0.9901 - val_loss: 9.7491 - val_sparse_categorical_accuracy: 0.1979\n",
      "Epoch 116/10000\n",
      " 37/932 [>.............................] - ETA: 2s - loss: 0.0907 - sparse_categorical_accuracy: 0.9932\n",
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      "Epoch 116: saving model to training_1\\cp.ckpt\n",
      "916/932 [============================>.] - ETA: 0s - loss: 0.1030 - sparse_categorical_accuracy: 0.9905\n",
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      "924/932 [============================>.] - ETA: 0s - loss: 0.1029 - sparse_categorical_accuracy: 0.9905WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.1028 - sparse_categorical_accuracy: 0.9905 - val_loss: 9.7937 - val_sparse_categorical_accuracy: 0.1970\n",
      "Epoch 117/10000\n",
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      "Epoch 117: saving model to training_1\\cp.ckpt\n",
      "924/932 [============================>.] - ETA: 0s - loss: 0.1012 - sparse_categorical_accuracy: 0.9901WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.1014 - sparse_categorical_accuracy: 0.9899 - val_loss: 9.8403 - val_sparse_categorical_accuracy: 0.2003\n",
      "Epoch 118/10000\n",
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      "891/932 [===========================>..] - ETA: 0s - loss: 0.0998 - sparse_categorical_accuracy: 0.9905\n",
      "Epoch 118: saving model to training_1\\cp.ckpt\n",
      "917/932 [============================>.] - ETA: 0s - loss: 0.0996 - sparse_categorical_accuracy: 0.9907WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.1000 - sparse_categorical_accuracy: 0.9905 - val_loss: 9.8841 - val_sparse_categorical_accuracy: 0.1973\n",
      "Epoch 119/10000\n",
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      "Epoch 119: saving model to training_1\\cp.ckpt\n",
      "928/932 [============================>.] - ETA: 0s - loss: 0.0984 - sparse_categorical_accuracy: 0.9913WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 4s 4ms/step - loss: 0.0984 - sparse_categorical_accuracy: 0.9913 - val_loss: 9.9242 - val_sparse_categorical_accuracy: 0.1962\n",
      "Epoch 120/10000\n",
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      "869/932 [==========================>...] - ETA: 0s - loss: 0.0961 - sparse_categorical_accuracy: 0.9917\n",
      "Epoch 120: saving model to training_1\\cp.ckpt\n",
      "930/932 [============================>.] - ETA: 0s - loss: 0.0969 - sparse_categorical_accuracy: 0.9911WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.0969 - sparse_categorical_accuracy: 0.9911 - val_loss: 9.9715 - val_sparse_categorical_accuracy: 0.1979\n",
      "Epoch 121/10000\n",
      " 18/932 [..............................] - ETA: 2s - loss: 0.1023 - sparse_categorical_accuracy: 0.9861\n",
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      " 96/932 [==>...........................] - ETA: 2s - loss: 0.1003 - sparse_categorical_accuracy: 0.9876\n",
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      "494/932 [==============>...............] - ETA: 1s - loss: 0.0919 - sparse_categorical_accuracy: 0.9916\n",
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      "654/932 [====================>.........] - ETA: 1s - loss: 0.0921 - sparse_categorical_accuracy: 0.9915\n",
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      "743/932 [======================>.......] - ETA: 0s - loss: 0.0935 - sparse_categorical_accuracy: 0.9911\n",
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      "815/932 [=========================>....] - ETA: 0s - loss: 0.0948 - sparse_categorical_accuracy: 0.9909\n",
      "Epoch 121: saving model to training_1\\cp.ckpt\n",
      "898/932 [===========================>..] - ETA: 0s - loss: 0.0954 - sparse_categorical_accuracy: 0.9906\n",
      "Epoch 121: saving model to training_1\\cp.ckpt\n",
      "931/932 [============================>.] - ETA: 0s - loss: 0.0955 - sparse_categorical_accuracy: 0.9905WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 4s 4ms/step - loss: 0.0955 - sparse_categorical_accuracy: 0.9905 - val_loss: 10.0131 - val_sparse_categorical_accuracy: 0.1962\n",
      "Epoch 122/10000\n",
      " 42/932 [>.............................] - ETA: 3s - loss: 0.0918 - sparse_categorical_accuracy: 0.9926\n",
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      "Epoch 122: saving model to training_1\\cp.ckpt\n",
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      "Epoch 122: saving model to training_1\\cp.ckpt\n",
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      "683/932 [====================>.........] - ETA: 1s - loss: 0.0948 - sparse_categorical_accuracy: 0.9917\n",
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      "764/932 [=======================>......] - ETA: 0s - loss: 0.0946 - sparse_categorical_accuracy: 0.9917\n",
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      "919/932 [============================>.] - ETA: 0s - loss: 0.0941 - sparse_categorical_accuracy: 0.9916\n",
      "Epoch 122: saving model to training_1\\cp.ckpt\n",
      "932/932 [==============================] - ETA: 0s - loss: 0.0940 - sparse_categorical_accuracy: 0.9917WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 4s 4ms/step - loss: 0.0940 - sparse_categorical_accuracy: 0.9917 - val_loss: 10.0641 - val_sparse_categorical_accuracy: 0.1984\n",
      "Epoch 123/10000\n",
      " 76/932 [=>............................] - ETA: 2s - loss: 0.0881 - sparse_categorical_accuracy: 0.9942\n",
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      "Epoch 123: saving model to training_1\\cp.ckpt\n",
      "398/932 [===========>..................] - ETA: 1s - loss: 0.0890 - sparse_categorical_accuracy: 0.9923\n",
      "Epoch 123: saving model to training_1\\cp.ckpt\n",
      "470/932 [==============>...............] - ETA: 1s - loss: 0.0907 - sparse_categorical_accuracy: 0.9923\n",
      "Epoch 123: saving model to training_1\\cp.ckpt\n",
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      "Epoch 123: saving model to training_1\\cp.ckpt\n",
      "639/932 [===================>..........] - ETA: 1s - loss: 0.0901 - sparse_categorical_accuracy: 0.9923\n",
      "Epoch 123: saving model to training_1\\cp.ckpt\n",
      "705/932 [=====================>........] - ETA: 0s - loss: 0.0913 - sparse_categorical_accuracy: 0.9920\n",
      "Epoch 123: saving model to training_1\\cp.ckpt\n",
      "799/932 [========================>.....] - ETA: 0s - loss: 0.0913 - sparse_categorical_accuracy: 0.9921\n",
      "Epoch 123: saving model to training_1\\cp.ckpt\n",
      "877/932 [===========================>..] - ETA: 0s - loss: 0.0924 - sparse_categorical_accuracy: 0.9917\n",
      "Epoch 123: saving model to training_1\\cp.ckpt\n",
      "932/932 [==============================] - ETA: 0s - loss: 0.0926 - sparse_categorical_accuracy: 0.9916WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 4s 4ms/step - loss: 0.0926 - sparse_categorical_accuracy: 0.9916 - val_loss: 10.1043 - val_sparse_categorical_accuracy: 0.1979\n",
      "Epoch 124/10000\n",
      " 18/932 [..............................] - ETA: 2s - loss: 0.0696 - sparse_categorical_accuracy: 0.9965\n",
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      "Epoch 124: saving model to training_1\\cp.ckpt\n",
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      "Epoch 124: saving model to training_1\\cp.ckpt\n",
      "922/932 [============================>.] - ETA: 0s - loss: 0.0910 - sparse_categorical_accuracy: 0.9916WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 4s 4ms/step - loss: 0.0913 - sparse_categorical_accuracy: 0.9915 - val_loss: 10.1499 - val_sparse_categorical_accuracy: 0.1960\n",
      "Epoch 125/10000\n",
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      "Epoch 125: saving model to training_1\\cp.ckpt\n",
      "918/932 [============================>.] - ETA: 0s - loss: 0.0896 - sparse_categorical_accuracy: 0.9920WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 4s 4ms/step - loss: 0.0897 - sparse_categorical_accuracy: 0.9919 - val_loss: 10.1967 - val_sparse_categorical_accuracy: 0.1962\n",
      "Epoch 126/10000\n",
      "  1/932 [..............................] - ETA: 3s - loss: 0.0453 - sparse_categorical_accuracy: 1.0000\n",
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      "Epoch 126: saving model to training_1\\cp.ckpt\n",
      "919/932 [============================>.] - ETA: 0s - loss: 0.0882 - sparse_categorical_accuracy: 0.9920WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 4s 4ms/step - loss: 0.0883 - sparse_categorical_accuracy: 0.9919 - val_loss: 10.2442 - val_sparse_categorical_accuracy: 0.1968\n",
      "Epoch 127/10000\n",
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      "Epoch 127: saving model to training_1\\cp.ckpt\n",
      "928/932 [============================>.] - ETA: 0s - loss: 0.0870 - sparse_categorical_accuracy: 0.9927WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 4s 4ms/step - loss: 0.0871 - sparse_categorical_accuracy: 0.9925 - val_loss: 10.2844 - val_sparse_categorical_accuracy: 0.1981\n",
      "Epoch 128/10000\n",
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      "923/932 [============================>.] - ETA: 0s - loss: 0.0858 - sparse_categorical_accuracy: 0.9922WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 4s 4ms/step - loss: 0.0857 - sparse_categorical_accuracy: 0.9923 - val_loss: 10.3320 - val_sparse_categorical_accuracy: 0.1989\n",
      "Epoch 129/10000\n",
      "  1/932 [..............................] - ETA: 4s - loss: 0.0379 - sparse_categorical_accuracy: 1.0000\n",
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      " 82/932 [=>............................] - ETA: 3s - loss: 0.0845 - sparse_categorical_accuracy: 0.9924\n",
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      "Epoch 129: saving model to training_1\\cp.ckpt\n",
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      "Epoch 129: saving model to training_1\\cp.ckpt\n",
      "802/932 [========================>.....] - ETA: 0s - loss: 0.0837 - sparse_categorical_accuracy: 0.9923\n",
      "Epoch 129: saving model to training_1\\cp.ckpt\n",
      "885/932 [===========================>..] - ETA: 0s - loss: 0.0842 - sparse_categorical_accuracy: 0.9923\n",
      "Epoch 129: saving model to training_1\\cp.ckpt\n",
      "923/932 [============================>.] - ETA: 0s - loss: 0.0845 - sparse_categorical_accuracy: 0.9921WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 4s 4ms/step - loss: 0.0845 - sparse_categorical_accuracy: 0.9921 - val_loss: 10.3764 - val_sparse_categorical_accuracy: 0.1957\n",
      "Epoch 130/10000\n",
      " 33/932 [>.............................] - ETA: 2s - loss: 0.0707 - sparse_categorical_accuracy: 0.9962\n",
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      "Epoch 130: saving model to training_1\\cp.ckpt\n",
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      "673/932 [====================>.........] - ETA: 0s - loss: 0.0830 - sparse_categorical_accuracy: 0.9932\n",
      "Epoch 130: saving model to training_1\\cp.ckpt\n",
      "740/932 [======================>.......] - ETA: 0s - loss: 0.0827 - sparse_categorical_accuracy: 0.9933\n",
      "Epoch 130: saving model to training_1\\cp.ckpt\n",
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      "Epoch 130: saving model to training_1\\cp.ckpt\n",
      "912/932 [============================>.] - ETA: 0s - loss: 0.0830 - sparse_categorical_accuracy: 0.9931\n",
      "Epoch 130: saving model to training_1\\cp.ckpt\n",
      "918/932 [============================>.] - ETA: 0s - loss: 0.0831 - sparse_categorical_accuracy: 0.9931WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 4s 4ms/step - loss: 0.0831 - sparse_categorical_accuracy: 0.9930 - val_loss: 10.4205 - val_sparse_categorical_accuracy: 0.1954\n",
      "Epoch 131/10000\n",
      " 61/932 [>.............................] - ETA: 2s - loss: 0.0726 - sparse_categorical_accuracy: 0.9939\n",
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      "Epoch 131: saving model to training_1\\cp.ckpt\n",
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      "539/932 [================>.............] - ETA: 1s - loss: 0.0810 - sparse_categorical_accuracy: 0.9922\n",
      "Epoch 131: saving model to training_1\\cp.ckpt\n",
      "623/932 [===================>..........] - ETA: 1s - loss: 0.0809 - sparse_categorical_accuracy: 0.9922\n",
      "Epoch 131: saving model to training_1\\cp.ckpt\n",
      "702/932 [=====================>........] - ETA: 0s - loss: 0.0811 - sparse_categorical_accuracy: 0.9923\n",
      "Epoch 131: saving model to training_1\\cp.ckpt\n",
      "768/932 [=======================>......] - ETA: 0s - loss: 0.0819 - sparse_categorical_accuracy: 0.9924\n",
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      "858/932 [==========================>...] - ETA: 0s - loss: 0.0819 - sparse_categorical_accuracy: 0.9924\n",
      "Epoch 131: saving model to training_1\\cp.ckpt\n",
      "928/932 [============================>.] - ETA: 0s - loss: 0.0817 - sparse_categorical_accuracy: 0.9927WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 4s 4ms/step - loss: 0.0818 - sparse_categorical_accuracy: 0.9926 - val_loss: 10.4657 - val_sparse_categorical_accuracy: 0.1968\n",
      "Epoch 132/10000\n",
      "  1/932 [..............................] - ETA: 3s - loss: 0.0574 - sparse_categorical_accuracy: 1.0000\n",
      "Epoch 132: saving model to training_1\\cp.ckpt\n",
      " 89/932 [=>............................] - ETA: 3s - loss: 0.0659 - sparse_categorical_accuracy: 0.9986\n",
      "Epoch 132: saving model to training_1\\cp.ckpt\n",
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      "Epoch 132: saving model to training_1\\cp.ckpt\n",
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      "Epoch 132: saving model to training_1\\cp.ckpt\n",
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      "Epoch 132: saving model to training_1\\cp.ckpt\n",
      "731/932 [======================>.......] - ETA: 0s - loss: 0.0792 - sparse_categorical_accuracy: 0.9932\n",
      "Epoch 132: saving model to training_1\\cp.ckpt\n",
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      "Epoch 132: saving model to training_1\\cp.ckpt\n",
      "925/932 [============================>.] - ETA: 0s - loss: 0.0807 - sparse_categorical_accuracy: 0.9930WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 4s 4ms/step - loss: 0.0806 - sparse_categorical_accuracy: 0.9930 - val_loss: 10.5191 - val_sparse_categorical_accuracy: 0.1976\n",
      "Epoch 133/10000\n",
      " 33/932 [>.............................] - ETA: 2s - loss: 0.0821 - sparse_categorical_accuracy: 0.9924\n",
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      "Epoch 133: saving model to training_1\\cp.ckpt\n",
      "904/932 [============================>.] - ETA: 0s - loss: 0.0797 - sparse_categorical_accuracy: 0.9925\n",
      "Epoch 133: saving model to training_1\\cp.ckpt\n",
      "920/932 [============================>.] - ETA: 0s - loss: 0.0798 - sparse_categorical_accuracy: 0.9925WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 4s 4ms/step - loss: 0.0796 - sparse_categorical_accuracy: 0.9926 - val_loss: 10.5556 - val_sparse_categorical_accuracy: 0.1973\n",
      "Epoch 134/10000\n",
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      "Epoch 134: saving model to training_1\\cp.ckpt\n",
      "920/932 [============================>.] - ETA: 0s - loss: 0.0782 - sparse_categorical_accuracy: 0.9936WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 4s 4ms/step - loss: 0.0782 - sparse_categorical_accuracy: 0.9936 - val_loss: 10.6053 - val_sparse_categorical_accuracy: 0.1962\n",
      "Epoch 135/10000\n",
      "  1/932 [..............................] - ETA: 4s - loss: 0.1012 - sparse_categorical_accuracy: 1.0000\n",
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      "Epoch 135: saving model to training_1\\cp.ckpt\n",
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      "Epoch 135: saving model to training_1\\cp.ckpt\n",
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      "887/932 [===========================>..] - ETA: 0s - loss: 0.0766 - sparse_categorical_accuracy: 0.9932\n",
      "Epoch 135: saving model to training_1\\cp.ckpt\n",
      "932/932 [==============================] - ETA: 0s - loss: 0.0770 - sparse_categorical_accuracy: 0.9932WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.0770 - sparse_categorical_accuracy: 0.9932 - val_loss: 10.6506 - val_sparse_categorical_accuracy: 0.1984\n",
      "Epoch 136/10000\n",
      " 38/932 [>.............................] - ETA: 2s - loss: 0.0648 - sparse_categorical_accuracy: 0.9951\n",
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      "Epoch 136: saving model to training_1\\cp.ckpt\n",
      "915/932 [============================>.] - ETA: 0s - loss: 0.0758 - sparse_categorical_accuracy: 0.9933\n",
      "Epoch 136: saving model to training_1\\cp.ckpt\n",
      "924/932 [============================>.] - ETA: 0s - loss: 0.0757 - sparse_categorical_accuracy: 0.9934WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.0758 - sparse_categorical_accuracy: 0.9934 - val_loss: 10.6927 - val_sparse_categorical_accuracy: 0.1989\n",
      "Epoch 137/10000\n",
      " 54/932 [>.............................] - ETA: 2s - loss: 0.0691 - sparse_categorical_accuracy: 0.9942\n",
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      "Epoch 137: saving model to training_1\\cp.ckpt\n",
      "870/932 [===========================>..] - ETA: 0s - loss: 0.0743 - sparse_categorical_accuracy: 0.9936\n",
      "Epoch 137: saving model to training_1\\cp.ckpt\n",
      "931/932 [============================>.] - ETA: 0s - loss: 0.0744 - sparse_categorical_accuracy: 0.9938WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 4s 4ms/step - loss: 0.0744 - sparse_categorical_accuracy: 0.9938 - val_loss: 10.7359 - val_sparse_categorical_accuracy: 0.1976\n",
      "Epoch 138/10000\n",
      " 16/932 [..............................] - ETA: 3s - loss: 0.0595 - sparse_categorical_accuracy: 0.9961\n",
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      " 98/932 [==>...........................] - ETA: 3s - loss: 0.0691 - sparse_categorical_accuracy: 0.9943\n",
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      "Epoch 138: saving model to training_1\\cp.ckpt\n",
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      "Epoch 138: saving model to training_1\\cp.ckpt\n",
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      "736/932 [======================>.......] - ETA: 0s - loss: 0.0728 - sparse_categorical_accuracy: 0.9937\n",
      "Epoch 138: saving model to training_1\\cp.ckpt\n",
      "819/932 [=========================>....] - ETA: 0s - loss: 0.0732 - sparse_categorical_accuracy: 0.9936\n",
      "Epoch 138: saving model to training_1\\cp.ckpt\n",
      "899/932 [===========================>..] - ETA: 0s - loss: 0.0733 - sparse_categorical_accuracy: 0.9934\n",
      "Epoch 138: saving model to training_1\\cp.ckpt\n",
      "921/932 [============================>.] - ETA: 0s - loss: 0.0734 - sparse_categorical_accuracy: 0.9934WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 4s 4ms/step - loss: 0.0735 - sparse_categorical_accuracy: 0.9934 - val_loss: 10.7862 - val_sparse_categorical_accuracy: 0.1984\n",
      "Epoch 139/10000\n",
      " 33/932 [>.............................] - ETA: 2s - loss: 0.0611 - sparse_categorical_accuracy: 0.9981\n",
      "Epoch 139: saving model to training_1\\cp.ckpt\n",
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      "Epoch 139: saving model to training_1\\cp.ckpt\n",
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      "Epoch 139: saving model to training_1\\cp.ckpt\n",
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      "Epoch 139: saving model to training_1\\cp.ckpt\n",
      "367/932 [==========>...................] - ETA: 2s - loss: 0.0674 - sparse_categorical_accuracy: 0.9946\n",
      "Epoch 139: saving model to training_1\\cp.ckpt\n",
      "434/932 [============>.................] - ETA: 1s - loss: 0.0675 - sparse_categorical_accuracy: 0.9947\n",
      "Epoch 139: saving model to training_1\\cp.ckpt\n",
      "526/932 [===============>..............] - ETA: 1s - loss: 0.0680 - sparse_categorical_accuracy: 0.9941\n",
      "Epoch 139: saving model to training_1\\cp.ckpt\n",
      "592/932 [==================>...........] - ETA: 1s - loss: 0.0680 - sparse_categorical_accuracy: 0.9944\n",
      "Epoch 139: saving model to training_1\\cp.ckpt\n",
      "681/932 [====================>.........] - ETA: 0s - loss: 0.0703 - sparse_categorical_accuracy: 0.9942\n",
      "Epoch 139: saving model to training_1\\cp.ckpt\n",
      "764/932 [=======================>......] - ETA: 0s - loss: 0.0708 - sparse_categorical_accuracy: 0.9941\n",
      "Epoch 139: saving model to training_1\\cp.ckpt\n",
      "833/932 [=========================>....] - ETA: 0s - loss: 0.0719 - sparse_categorical_accuracy: 0.9938\n",
      "Epoch 139: saving model to training_1\\cp.ckpt\n",
      "925/932 [============================>.] - ETA: 0s - loss: 0.0722 - sparse_categorical_accuracy: 0.9939\n",
      "Epoch 139: saving model to training_1\\cp.ckpt\n",
      "932/932 [==============================] - ETA: 0s - loss: 0.0724 - sparse_categorical_accuracy: 0.9939WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 4s 4ms/step - loss: 0.0724 - sparse_categorical_accuracy: 0.9939 - val_loss: 10.8328 - val_sparse_categorical_accuracy: 0.2000\n",
      "Epoch 140/10000\n",
      " 63/932 [=>............................] - ETA: 2s - loss: 0.0722 - sparse_categorical_accuracy: 0.9950\n",
      "Epoch 140: saving model to training_1\\cp.ckpt\n",
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      "Epoch 140: saving model to training_1\\cp.ckpt\n",
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      "Epoch 140: saving model to training_1\\cp.ckpt\n",
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      "Epoch 140: saving model to training_1\\cp.ckpt\n",
      "390/932 [===========>..................] - ETA: 2s - loss: 0.0671 - sparse_categorical_accuracy: 0.9952\n",
      "Epoch 140: saving model to training_1\\cp.ckpt\n",
      "475/932 [==============>...............] - ETA: 1s - loss: 0.0689 - sparse_categorical_accuracy: 0.9945\n",
      "Epoch 140: saving model to training_1\\cp.ckpt\n",
      "542/932 [================>.............] - ETA: 1s - loss: 0.0688 - sparse_categorical_accuracy: 0.9945\n",
      "Epoch 140: saving model to training_1\\cp.ckpt\n",
      "635/932 [===================>..........] - ETA: 1s - loss: 0.0687 - sparse_categorical_accuracy: 0.9948\n",
      "Epoch 140: saving model to training_1\\cp.ckpt\n",
      "715/932 [======================>.......] - ETA: 0s - loss: 0.0688 - sparse_categorical_accuracy: 0.9949\n",
      "Epoch 140: saving model to training_1\\cp.ckpt\n",
      "782/932 [========================>.....] - ETA: 0s - loss: 0.0692 - sparse_categorical_accuracy: 0.9945\n",
      "Epoch 140: saving model to training_1\\cp.ckpt\n",
      "871/932 [===========================>..] - ETA: 0s - loss: 0.0710 - sparse_categorical_accuracy: 0.9940\n",
      "Epoch 140: saving model to training_1\\cp.ckpt\n",
      "922/932 [============================>.] - ETA: 0s - loss: 0.0713 - sparse_categorical_accuracy: 0.9939WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 4s 4ms/step - loss: 0.0713 - sparse_categorical_accuracy: 0.9938 - val_loss: 10.8763 - val_sparse_categorical_accuracy: 0.1973\n",
      "Epoch 141/10000\n",
      " 16/932 [..............................] - ETA: 3s - loss: 0.0464 - sparse_categorical_accuracy: 1.0000\n",
      "Epoch 141: saving model to training_1\\cp.ckpt\n",
      "102/932 [==>...........................] - ETA: 3s - loss: 0.0636 - sparse_categorical_accuracy: 0.9945\n",
      "Epoch 141: saving model to training_1\\cp.ckpt\n",
      "183/932 [====>.........................] - ETA: 2s - loss: 0.0616 - sparse_categorical_accuracy: 0.9962\n",
      "Epoch 141: saving model to training_1\\cp.ckpt\n",
      "263/932 [=======>......................] - ETA: 2s - loss: 0.0623 - sparse_categorical_accuracy: 0.9962\n",
      "Epoch 141: saving model to training_1\\cp.ckpt\n",
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      "Epoch 141: saving model to training_1\\cp.ckpt\n",
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      "651/932 [===================>..........] - ETA: 1s - loss: 0.0681 - sparse_categorical_accuracy: 0.9945\n",
      "Epoch 141: saving model to training_1\\cp.ckpt\n",
      "741/932 [======================>.......] - ETA: 0s - loss: 0.0686 - sparse_categorical_accuracy: 0.9947\n",
      "Epoch 141: saving model to training_1\\cp.ckpt\n",
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      "Epoch 141: saving model to training_1\\cp.ckpt\n",
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      "Epoch 141: saving model to training_1\\cp.ckpt\n",
      "921/932 [============================>.] - ETA: 0s - loss: 0.0704 - sparse_categorical_accuracy: 0.9941WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 4s 4ms/step - loss: 0.0703 - sparse_categorical_accuracy: 0.9942 - val_loss: 10.9219 - val_sparse_categorical_accuracy: 0.1997\n",
      "Epoch 142/10000\n",
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      "929/932 [============================>.] - ETA: 0s - loss: 0.0692 - sparse_categorical_accuracy: 0.9939\n",
      "Epoch 142: saving model to training_1\\cp.ckpt\n",
      "WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 4s 4ms/step - loss: 0.0692 - sparse_categorical_accuracy: 0.9940 - val_loss: 10.9793 - val_sparse_categorical_accuracy: 0.1995\n",
      "Epoch 143/10000\n",
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      "785/932 [========================>.....] - ETA: 0s - loss: 0.0666 - sparse_categorical_accuracy: 0.9942\n",
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      "878/932 [===========================>..] - ETA: 0s - loss: 0.0675 - sparse_categorical_accuracy: 0.9942\n",
      "Epoch 143: saving model to training_1\\cp.ckpt\n",
      "929/932 [============================>.] - ETA: 0s - loss: 0.0681 - sparse_categorical_accuracy: 0.9940WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 4s 4ms/step - loss: 0.0680 - sparse_categorical_accuracy: 0.9940 - val_loss: 11.0126 - val_sparse_categorical_accuracy: 0.1973\n",
      "Epoch 144/10000\n",
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      "924/932 [============================>.] - ETA: 0s - loss: 0.0670 - sparse_categorical_accuracy: 0.9943WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 4s 4ms/step - loss: 0.0670 - sparse_categorical_accuracy: 0.9942 - val_loss: 11.0622 - val_sparse_categorical_accuracy: 0.1979\n",
      "Epoch 145/10000\n",
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      "Epoch 145: saving model to training_1\\cp.ckpt\n",
      "932/932 [==============================] - ETA: 0s - loss: 0.0659 - sparse_categorical_accuracy: 0.9946WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 4s 4ms/step - loss: 0.0659 - sparse_categorical_accuracy: 0.9946 - val_loss: 11.1080 - val_sparse_categorical_accuracy: 0.1979\n",
      "Epoch 146/10000\n",
      "  1/932 [..............................] - ETA: 4s - loss: 0.0408 - sparse_categorical_accuracy: 1.0000\n",
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      " 83/932 [=>............................] - ETA: 3s - loss: 0.0587 - sparse_categorical_accuracy: 0.9955\n",
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      "873/932 [===========================>..] - ETA: 0s - loss: 0.0648 - sparse_categorical_accuracy: 0.9941\n",
      "Epoch 146: saving model to training_1\\cp.ckpt\n",
      "918/932 [============================>.] - ETA: 0s - loss: 0.0649 - sparse_categorical_accuracy: 0.9941WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 4s 4ms/step - loss: 0.0648 - sparse_categorical_accuracy: 0.9940 - val_loss: 11.1538 - val_sparse_categorical_accuracy: 0.1970\n",
      "Epoch 147/10000\n",
      " 20/932 [..............................] - ETA: 2s - loss: 0.0519 - sparse_categorical_accuracy: 1.0000\n",
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      "904/932 [============================>.] - ETA: 0s - loss: 0.0638 - sparse_categorical_accuracy: 0.9946\n",
      "Epoch 147: saving model to training_1\\cp.ckpt\n",
      "930/932 [============================>.] - ETA: 0s - loss: 0.0638 - sparse_categorical_accuracy: 0.9945WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.0640 - sparse_categorical_accuracy: 0.9944 - val_loss: 11.2015 - val_sparse_categorical_accuracy: 0.1976\n",
      "Epoch 148/10000\n",
      " 53/932 [>.............................] - ETA: 2s - loss: 0.0542 - sparse_categorical_accuracy: 0.9988\n",
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      "Epoch 148: saving model to training_1\\cp.ckpt\n",
      "921/932 [============================>.] - ETA: 0s - loss: 0.0630 - sparse_categorical_accuracy: 0.9943WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 4s 4ms/step - loss: 0.0630 - sparse_categorical_accuracy: 0.9944 - val_loss: 11.2414 - val_sparse_categorical_accuracy: 0.1962\n",
      "Epoch 149/10000\n",
      "  1/932 [..............................] - ETA: 4s - loss: 0.0518 - sparse_categorical_accuracy: 1.0000\n",
      "Epoch 149: saving model to training_1\\cp.ckpt\n",
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      "639/932 [===================>..........] - ETA: 0s - loss: 0.0608 - sparse_categorical_accuracy: 0.9950\n",
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      "720/932 [======================>.......] - ETA: 0s - loss: 0.0612 - sparse_categorical_accuracy: 0.9948\n",
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      "Epoch 149: saving model to training_1\\cp.ckpt\n",
      "878/932 [===========================>..] - ETA: 0s - loss: 0.0621 - sparse_categorical_accuracy: 0.9945\n",
      "Epoch 149: saving model to training_1\\cp.ckpt\n",
      "923/932 [============================>.] - ETA: 0s - loss: 0.0621 - sparse_categorical_accuracy: 0.9945WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.0620 - sparse_categorical_accuracy: 0.9945 - val_loss: 11.2929 - val_sparse_categorical_accuracy: 0.1995\n",
      "Epoch 150/10000\n",
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      "916/932 [============================>.] - ETA: 0s - loss: 0.0605 - sparse_categorical_accuracy: 0.9944WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.0611 - sparse_categorical_accuracy: 0.9942 - val_loss: 11.3308 - val_sparse_categorical_accuracy: 0.1979\n",
      "Epoch 151/10000\n",
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      "917/932 [============================>.] - ETA: 0s - loss: 0.0601 - sparse_categorical_accuracy: 0.9948WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.0600 - sparse_categorical_accuracy: 0.9949 - val_loss: 11.3812 - val_sparse_categorical_accuracy: 0.1962\n",
      "Epoch 152/10000\n",
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      "928/932 [============================>.] - ETA: 0s - loss: 0.0592 - sparse_categorical_accuracy: 0.9945WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.0592 - sparse_categorical_accuracy: 0.9945 - val_loss: 11.4256 - val_sparse_categorical_accuracy: 0.1973\n",
      "Epoch 153/10000\n",
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      "920/932 [============================>.] - ETA: 0s - loss: 0.0582 - sparse_categorical_accuracy: 0.9944WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.0583 - sparse_categorical_accuracy: 0.9944 - val_loss: 11.4770 - val_sparse_categorical_accuracy: 0.1965\n",
      "Epoch 154/10000\n",
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      "922/932 [============================>.] - ETA: 0s - loss: 0.0573 - sparse_categorical_accuracy: 0.9948WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.0574 - sparse_categorical_accuracy: 0.9948 - val_loss: 11.5246 - val_sparse_categorical_accuracy: 0.1968\n",
      "Epoch 155/10000\n",
      "  1/932 [..............................] - ETA: 4s - loss: 0.1217 - sparse_categorical_accuracy: 1.0000\n",
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      "Epoch 155: saving model to training_1\\cp.ckpt\n",
      "932/932 [==============================] - ETA: 0s - loss: 0.0565 - sparse_categorical_accuracy: 0.9945WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.0565 - sparse_categorical_accuracy: 0.9945 - val_loss: 11.5691 - val_sparse_categorical_accuracy: 0.1976\n",
      "Epoch 156/10000\n",
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      "924/932 [============================>.] - ETA: 0s - loss: 0.0556 - sparse_categorical_accuracy: 0.9949WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.0556 - sparse_categorical_accuracy: 0.9948 - val_loss: 11.6186 - val_sparse_categorical_accuracy: 0.1957\n",
      "Epoch 157/10000\n",
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      "926/932 [============================>.] - ETA: 0s - loss: 0.0547 - sparse_categorical_accuracy: 0.9947WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.0547 - sparse_categorical_accuracy: 0.9947 - val_loss: 11.6622 - val_sparse_categorical_accuracy: 0.1970\n",
      "Epoch 158/10000\n",
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      "Epoch 158: saving model to training_1\\cp.ckpt\n",
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      "Epoch 158: saving model to training_1\\cp.ckpt\n",
      "917/932 [============================>.] - ETA: 0s - loss: 0.0540 - sparse_categorical_accuracy: 0.9950WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.0540 - sparse_categorical_accuracy: 0.9950 - val_loss: 11.7090 - val_sparse_categorical_accuracy: 0.1970\n",
      "Epoch 159/10000\n",
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      "Epoch 159: saving model to training_1\\cp.ckpt\n",
      "919/932 [============================>.] - ETA: 0s - loss: 0.0530 - sparse_categorical_accuracy: 0.9946\n",
      "Epoch 159: saving model to training_1\\cp.ckpt\n",
      "928/932 [============================>.] - ETA: 0s - loss: 0.0530 - sparse_categorical_accuracy: 0.9945WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.0530 - sparse_categorical_accuracy: 0.9945 - val_loss: 11.7498 - val_sparse_categorical_accuracy: 0.1973\n",
      "Epoch 160/10000\n",
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      "867/932 [==========================>...] - ETA: 0s - loss: 0.0519 - sparse_categorical_accuracy: 0.9952\n",
      "Epoch 160: saving model to training_1\\cp.ckpt\n",
      "927/932 [============================>.] - ETA: 0s - loss: 0.0523 - sparse_categorical_accuracy: 0.9949WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.0522 - sparse_categorical_accuracy: 0.9950 - val_loss: 11.8009 - val_sparse_categorical_accuracy: 0.1965\n",
      "Epoch 161/10000\n",
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      "896/932 [===========================>..] - ETA: 0s - loss: 0.0515 - sparse_categorical_accuracy: 0.9953\n",
      "Epoch 161: saving model to training_1\\cp.ckpt\n",
      "921/932 [============================>.] - ETA: 0s - loss: 0.0516 - sparse_categorical_accuracy: 0.9952WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.0515 - sparse_categorical_accuracy: 0.9953 - val_loss: 11.8439 - val_sparse_categorical_accuracy: 0.1968\n",
      "Epoch 162/10000\n",
      " 36/932 [>.............................] - ETA: 2s - loss: 0.0486 - sparse_categorical_accuracy: 0.9948\n",
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      "Epoch 162: saving model to training_1\\cp.ckpt\n",
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      "921/932 [============================>.] - ETA: 0s - loss: 0.0508 - sparse_categorical_accuracy: 0.9948\n",
      "Epoch 162: saving model to training_1\\cp.ckpt\n",
      "932/932 [==============================] - ETA: 0s - loss: 0.0507 - sparse_categorical_accuracy: 0.9949WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 4s 4ms/step - loss: 0.0507 - sparse_categorical_accuracy: 0.9949 - val_loss: 11.8942 - val_sparse_categorical_accuracy: 0.1968\n",
      "Epoch 163/10000\n",
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      "Epoch 163: saving model to training_1\\cp.ckpt\n",
      "791/932 [========================>.....] - ETA: 0s - loss: 0.0492 - sparse_categorical_accuracy: 0.9949\n",
      "Epoch 163: saving model to training_1\\cp.ckpt\n",
      "870/932 [===========================>..] - ETA: 0s - loss: 0.0498 - sparse_categorical_accuracy: 0.9948\n",
      "Epoch 163: saving model to training_1\\cp.ckpt\n",
      "931/932 [============================>.] - ETA: 0s - loss: 0.0500 - sparse_categorical_accuracy: 0.9949WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.0500 - sparse_categorical_accuracy: 0.9949 - val_loss: 11.9434 - val_sparse_categorical_accuracy: 0.1965\n",
      "Epoch 164/10000\n",
      " 19/932 [..............................] - ETA: 2s - loss: 0.0469 - sparse_categorical_accuracy: 0.9934\n",
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      "Epoch 164: saving model to training_1\\cp.ckpt\n",
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      "Epoch 164: saving model to training_1\\cp.ckpt\n",
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      "Epoch 164: saving model to training_1\\cp.ckpt\n",
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      "Epoch 164: saving model to training_1\\cp.ckpt\n",
      "907/932 [============================>.] - ETA: 0s - loss: 0.0489 - sparse_categorical_accuracy: 0.9952\n",
      "Epoch 164: saving model to training_1\\cp.ckpt\n",
      "925/932 [============================>.] - ETA: 0s - loss: 0.0489 - sparse_categorical_accuracy: 0.9951WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.0492 - sparse_categorical_accuracy: 0.9950 - val_loss: 11.9828 - val_sparse_categorical_accuracy: 0.1954\n",
      "Epoch 165/10000\n",
      " 54/932 [>.............................] - ETA: 2s - loss: 0.0401 - sparse_categorical_accuracy: 0.9988\n",
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      "Epoch 165: saving model to training_1\\cp.ckpt\n",
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      "Epoch 165: saving model to training_1\\cp.ckpt\n",
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      "Epoch 165: saving model to training_1\\cp.ckpt\n",
      "527/932 [===============>..............] - ETA: 1s - loss: 0.0472 - sparse_categorical_accuracy: 0.9955\n",
      "Epoch 165: saving model to training_1\\cp.ckpt\n",
      "609/932 [==================>...........] - ETA: 1s - loss: 0.0476 - sparse_categorical_accuracy: 0.9957\n",
      "Epoch 165: saving model to training_1\\cp.ckpt\n",
      "685/932 [=====================>........] - ETA: 0s - loss: 0.0474 - sparse_categorical_accuracy: 0.9957\n",
      "Epoch 165: saving model to training_1\\cp.ckpt\n",
      "766/932 [=======================>......] - ETA: 0s - loss: 0.0480 - sparse_categorical_accuracy: 0.9956\n",
      "Epoch 165: saving model to training_1\\cp.ckpt\n",
      "845/932 [==========================>...] - ETA: 0s - loss: 0.0483 - sparse_categorical_accuracy: 0.9953\n",
      "Epoch 165: saving model to training_1\\cp.ckpt\n",
      "926/932 [============================>.] - ETA: 0s - loss: 0.0485 - sparse_categorical_accuracy: 0.9953WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.0485 - sparse_categorical_accuracy: 0.9954 - val_loss: 12.0351 - val_sparse_categorical_accuracy: 0.1970\n",
      "Epoch 166/10000\n",
      "  1/932 [..............................] - ETA: 4s - loss: 0.0121 - sparse_categorical_accuracy: 1.0000\n",
      "Epoch 166: saving model to training_1\\cp.ckpt\n",
      " 80/932 [=>............................] - ETA: 2s - loss: 0.0455 - sparse_categorical_accuracy: 0.9945\n",
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      "Epoch 166: saving model to training_1\\cp.ckpt\n",
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      "Epoch 166: saving model to training_1\\cp.ckpt\n",
      "396/932 [===========>..................] - ETA: 1s - loss: 0.0451 - sparse_categorical_accuracy: 0.9956\n",
      "Epoch 166: saving model to training_1\\cp.ckpt\n",
      "473/932 [==============>...............] - ETA: 1s - loss: 0.0451 - sparse_categorical_accuracy: 0.9960\n",
      "Epoch 166: saving model to training_1\\cp.ckpt\n",
      "554/932 [================>.............] - ETA: 1s - loss: 0.0453 - sparse_categorical_accuracy: 0.9962\n",
      "Epoch 166: saving model to training_1\\cp.ckpt\n",
      "636/932 [===================>..........] - ETA: 0s - loss: 0.0463 - sparse_categorical_accuracy: 0.9955\n",
      "Epoch 166: saving model to training_1\\cp.ckpt\n",
      "715/932 [======================>.......] - ETA: 0s - loss: 0.0465 - sparse_categorical_accuracy: 0.9955\n",
      "Epoch 166: saving model to training_1\\cp.ckpt\n",
      "792/932 [========================>.....] - ETA: 0s - loss: 0.0472 - sparse_categorical_accuracy: 0.9954\n",
      "Epoch 166: saving model to training_1\\cp.ckpt\n",
      "874/932 [===========================>..] - ETA: 0s - loss: 0.0471 - sparse_categorical_accuracy: 0.9954\n",
      "Epoch 166: saving model to training_1\\cp.ckpt\n",
      "920/932 [============================>.] - ETA: 0s - loss: 0.0474 - sparse_categorical_accuracy: 0.9952WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.0476 - sparse_categorical_accuracy: 0.9952 - val_loss: 12.0805 - val_sparse_categorical_accuracy: 0.1960\n",
      "Epoch 167/10000\n",
      " 19/932 [..............................] - ETA: 2s - loss: 0.0444 - sparse_categorical_accuracy: 0.9967\n",
      "Epoch 167: saving model to training_1\\cp.ckpt\n",
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      "Epoch 167: saving model to training_1\\cp.ckpt\n",
      "902/932 [============================>.] - ETA: 0s - loss: 0.0469 - sparse_categorical_accuracy: 0.9951\n",
      "Epoch 167: saving model to training_1\\cp.ckpt\n",
      "930/932 [============================>.] - ETA: 0s - loss: 0.0470 - sparse_categorical_accuracy: 0.9951WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.0470 - sparse_categorical_accuracy: 0.9951 - val_loss: 12.1269 - val_sparse_categorical_accuracy: 0.1973\n",
      "Epoch 168/10000\n",
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      "691/932 [=====================>........] - ETA: 0s - loss: 0.0452 - sparse_categorical_accuracy: 0.9955\n",
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      "Epoch 168: saving model to training_1\\cp.ckpt\n",
      "931/932 [============================>.] - ETA: 0s - loss: 0.0463 - sparse_categorical_accuracy: 0.9950WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.0463 - sparse_categorical_accuracy: 0.9950 - val_loss: 12.1723 - val_sparse_categorical_accuracy: 0.1965\n",
      "Epoch 169/10000\n",
      "  1/932 [..............................] - ETA: 4s - loss: 0.0740 - sparse_categorical_accuracy: 1.0000\n",
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      "881/932 [===========================>..] - ETA: 0s - loss: 0.0452 - sparse_categorical_accuracy: 0.9950\n",
      "Epoch 169: saving model to training_1\\cp.ckpt\n",
      "924/932 [============================>.] - ETA: 0s - loss: 0.0454 - sparse_categorical_accuracy: 0.9950WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.0454 - sparse_categorical_accuracy: 0.9950 - val_loss: 12.2176 - val_sparse_categorical_accuracy: 0.1962\n",
      "Epoch 170/10000\n",
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      "907/932 [============================>.] - ETA: 0s - loss: 0.0449 - sparse_categorical_accuracy: 0.9952\n",
      "Epoch 170: saving model to training_1\\cp.ckpt\n",
      "916/932 [============================>.] - ETA: 0s - loss: 0.0448 - sparse_categorical_accuracy: 0.9953WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.0449 - sparse_categorical_accuracy: 0.9953 - val_loss: 12.2673 - val_sparse_categorical_accuracy: 0.1973\n",
      "Epoch 171/10000\n",
      " 54/932 [>.............................] - ETA: 2s - loss: 0.0370 - sparse_categorical_accuracy: 0.9988\n",
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      "917/932 [============================>.] - ETA: 0s - loss: 0.0442 - sparse_categorical_accuracy: 0.9956WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.0441 - sparse_categorical_accuracy: 0.9956 - val_loss: 12.3148 - val_sparse_categorical_accuracy: 0.1952\n",
      "Epoch 172/10000\n",
      "  1/932 [..............................] - ETA: 4s - loss: 0.0961 - sparse_categorical_accuracy: 0.9375\n",
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      "720/932 [======================>.......] - ETA: 0s - loss: 0.0430 - sparse_categorical_accuracy: 0.9951\n",
      "Epoch 172: saving model to training_1\\cp.ckpt\n",
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      "Epoch 172: saving model to training_1\\cp.ckpt\n",
      "883/932 [===========================>..] - ETA: 0s - loss: 0.0432 - sparse_categorical_accuracy: 0.9952\n",
      "Epoch 172: saving model to training_1\\cp.ckpt\n",
      "928/932 [============================>.] - ETA: 0s - loss: 0.0435 - sparse_categorical_accuracy: 0.9952WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.0435 - sparse_categorical_accuracy: 0.9952 - val_loss: 12.3639 - val_sparse_categorical_accuracy: 0.1957\n",
      "Epoch 173/10000\n",
      " 37/932 [>.............................] - ETA: 2s - loss: 0.0385 - sparse_categorical_accuracy: 0.9949\n",
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      "Epoch 173: saving model to training_1\\cp.ckpt\n",
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      "Epoch 173: saving model to training_1\\cp.ckpt\n",
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      "Epoch 173: saving model to training_1\\cp.ckpt\n",
      "671/932 [====================>.........] - ETA: 0s - loss: 0.0410 - sparse_categorical_accuracy: 0.9963\n",
      "Epoch 173: saving model to training_1\\cp.ckpt\n",
      "750/932 [=======================>......] - ETA: 0s - loss: 0.0416 - sparse_categorical_accuracy: 0.9960\n",
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      "829/932 [=========================>....] - ETA: 0s - loss: 0.0422 - sparse_categorical_accuracy: 0.9958\n",
      "Epoch 173: saving model to training_1\\cp.ckpt\n",
      "908/932 [============================>.] - ETA: 0s - loss: 0.0426 - sparse_categorical_accuracy: 0.9955\n",
      "Epoch 173: saving model to training_1\\cp.ckpt\n",
      "920/932 [============================>.] - ETA: 0s - loss: 0.0427 - sparse_categorical_accuracy: 0.9955WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.0427 - sparse_categorical_accuracy: 0.9954 - val_loss: 12.4077 - val_sparse_categorical_accuracy: 0.1957\n",
      "Epoch 174/10000\n",
      " 55/932 [>.............................] - ETA: 2s - loss: 0.0387 - sparse_categorical_accuracy: 0.9977\n",
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      "Epoch 174: saving model to training_1\\cp.ckpt\n",
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      "Epoch 174: saving model to training_1\\cp.ckpt\n",
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      "Epoch 174: saving model to training_1\\cp.ckpt\n",
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      "Epoch 174: saving model to training_1\\cp.ckpt\n",
      "539/932 [================>.............] - ETA: 1s - loss: 0.0419 - sparse_categorical_accuracy: 0.9952\n",
      "Epoch 174: saving model to training_1\\cp.ckpt\n",
      "617/932 [==================>...........] - ETA: 1s - loss: 0.0418 - sparse_categorical_accuracy: 0.9952\n",
      "Epoch 174: saving model to training_1\\cp.ckpt\n",
      "699/932 [=====================>........] - ETA: 0s - loss: 0.0417 - sparse_categorical_accuracy: 0.9955\n",
      "Epoch 174: saving model to training_1\\cp.ckpt\n",
      "776/932 [=======================>......] - ETA: 0s - loss: 0.0418 - sparse_categorical_accuracy: 0.9953\n",
      "Epoch 174: saving model to training_1\\cp.ckpt\n",
      "857/932 [==========================>...] - ETA: 0s - loss: 0.0421 - sparse_categorical_accuracy: 0.9951\n",
      "Epoch 174: saving model to training_1\\cp.ckpt\n",
      "922/932 [============================>.] - ETA: 0s - loss: 0.0423 - sparse_categorical_accuracy: 0.9952WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.0423 - sparse_categorical_accuracy: 0.9952 - val_loss: 12.4550 - val_sparse_categorical_accuracy: 0.1952\n",
      "Epoch 175/10000\n",
      "  1/932 [..............................] - ETA: 4s - loss: 0.0777 - sparse_categorical_accuracy: 1.0000\n",
      "Epoch 175: saving model to training_1\\cp.ckpt\n",
      " 87/932 [=>............................] - ETA: 2s - loss: 0.0397 - sparse_categorical_accuracy: 0.9957\n",
      "Epoch 175: saving model to training_1\\cp.ckpt\n",
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      "Epoch 175: saving model to training_1\\cp.ckpt\n",
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      "Epoch 175: saving model to training_1\\cp.ckpt\n",
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      "Epoch 175: saving model to training_1\\cp.ckpt\n",
      "483/932 [==============>...............] - ETA: 1s - loss: 0.0398 - sparse_categorical_accuracy: 0.9966\n",
      "Epoch 175: saving model to training_1\\cp.ckpt\n",
      "562/932 [=================>............] - ETA: 1s - loss: 0.0398 - sparse_categorical_accuracy: 0.9967\n",
      "Epoch 175: saving model to training_1\\cp.ckpt\n",
      "648/932 [===================>..........] - ETA: 1s - loss: 0.0405 - sparse_categorical_accuracy: 0.9962\n",
      "Epoch 175: saving model to training_1\\cp.ckpt\n",
      "735/932 [======================>.......] - ETA: 0s - loss: 0.0410 - sparse_categorical_accuracy: 0.9960\n",
      "Epoch 175: saving model to training_1\\cp.ckpt\n",
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      "891/932 [===========================>..] - ETA: 0s - loss: 0.0415 - sparse_categorical_accuracy: 0.9956\n",
      "Epoch 175: saving model to training_1\\cp.ckpt\n",
      "931/932 [============================>.] - ETA: 0s - loss: 0.0416 - sparse_categorical_accuracy: 0.9954WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 4s 4ms/step - loss: 0.0416 - sparse_categorical_accuracy: 0.9954 - val_loss: 12.4964 - val_sparse_categorical_accuracy: 0.1962\n",
      "Epoch 176/10000\n",
      " 37/932 [>.............................] - ETA: 2s - loss: 0.0333 - sparse_categorical_accuracy: 0.9966\n",
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      "Epoch 176: saving model to training_1\\cp.ckpt\n",
      "916/932 [============================>.] - ETA: 0s - loss: 0.0407 - sparse_categorical_accuracy: 0.9956\n",
      "Epoch 176: saving model to training_1\\cp.ckpt\n",
      "924/932 [============================>.] - ETA: 0s - loss: 0.0409 - sparse_categorical_accuracy: 0.9955WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.0409 - sparse_categorical_accuracy: 0.9954 - val_loss: 12.5453 - val_sparse_categorical_accuracy: 0.1970\n",
      "Epoch 177/10000\n",
      " 55/932 [>.............................] - ETA: 2s - loss: 0.0384 - sparse_categorical_accuracy: 0.9943\n",
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      "862/932 [==========================>...] - ETA: 0s - loss: 0.0401 - sparse_categorical_accuracy: 0.9955\n",
      "Epoch 177: saving model to training_1\\cp.ckpt\n",
      "924/932 [============================>.] - ETA: 0s - loss: 0.0406 - sparse_categorical_accuracy: 0.9954WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.0405 - sparse_categorical_accuracy: 0.9954 - val_loss: 12.5934 - val_sparse_categorical_accuracy: 0.1970\n",
      "Epoch 178/10000\n",
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      "890/932 [===========================>..] - ETA: 0s - loss: 0.0394 - sparse_categorical_accuracy: 0.9958\n",
      "Epoch 178: saving model to training_1\\cp.ckpt\n",
      "917/932 [============================>.] - ETA: 0s - loss: 0.0394 - sparse_categorical_accuracy: 0.9958WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 3s 4ms/step - loss: 0.0396 - sparse_categorical_accuracy: 0.9956 - val_loss: 12.6402 - val_sparse_categorical_accuracy: 0.1968\n",
      "Epoch 179/10000\n",
      " 36/932 [>.............................] - ETA: 2s - loss: 0.0310 - sparse_categorical_accuracy: 0.9983\n",
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      "Epoch 179: saving model to training_1\\cp.ckpt\n",
      "924/932 [============================>.] - ETA: 0s - loss: 0.0393 - sparse_categorical_accuracy: 0.9952\n",
      "Epoch 179: saving model to training_1\\cp.ckpt\n",
      "929/932 [============================>.] - ETA: 0s - loss: 0.0392 - sparse_categorical_accuracy: 0.9952WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 4s 4ms/step - loss: 0.0392 - sparse_categorical_accuracy: 0.9952 - val_loss: 12.6886 - val_sparse_categorical_accuracy: 0.1973\n",
      "Epoch 180/10000\n",
      " 63/932 [=>............................] - ETA: 2s - loss: 0.0395 - sparse_categorical_accuracy: 0.9950\n",
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      "786/932 [========================>.....] - ETA: 0s - loss: 0.0375 - sparse_categorical_accuracy: 0.9957\n",
      "Epoch 180: saving model to training_1\\cp.ckpt\n",
      "865/932 [==========================>...] - ETA: 0s - loss: 0.0384 - sparse_categorical_accuracy: 0.9952\n",
      "Epoch 180: saving model to training_1\\cp.ckpt\n",
      "929/932 [============================>.] - ETA: 0s - loss: 0.0385 - sparse_categorical_accuracy: 0.9953WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 4s 4ms/step - loss: 0.0385 - sparse_categorical_accuracy: 0.9953 - val_loss: 12.7348 - val_sparse_categorical_accuracy: 0.1970\n",
      "Epoch 181/10000\n",
      " 19/932 [..............................] - ETA: 2s - loss: 0.0346 - sparse_categorical_accuracy: 0.9934\n",
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      "730/932 [======================>.......] - ETA: 0s - loss: 0.0374 - sparse_categorical_accuracy: 0.9953\n",
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      "814/932 [=========================>....] - ETA: 0s - loss: 0.0380 - sparse_categorical_accuracy: 0.9952\n",
      "Epoch 181: saving model to training_1\\cp.ckpt\n",
      "899/932 [===========================>..] - ETA: 0s - loss: 0.0383 - sparse_categorical_accuracy: 0.9951\n",
      "Epoch 181: saving model to training_1\\cp.ckpt\n",
      "920/932 [============================>.] - ETA: 0s - loss: 0.0382 - sparse_categorical_accuracy: 0.9952WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 4s 4ms/step - loss: 0.0381 - sparse_categorical_accuracy: 0.9953 - val_loss: 12.7841 - val_sparse_categorical_accuracy: 0.1957\n",
      "Epoch 182/10000\n",
      " 37/932 [>.............................] - ETA: 2s - loss: 0.0306 - sparse_categorical_accuracy: 0.9966\n",
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      "Epoch 182: saving model to training_1\\cp.ckpt\n",
      "364/932 [==========>...................] - ETA: 1s - loss: 0.0336 - sparse_categorical_accuracy: 0.9966\n",
      "Epoch 182: saving model to training_1\\cp.ckpt\n",
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      "Epoch 182: saving model to training_1\\cp.ckpt\n",
      "521/932 [===============>..............] - ETA: 1s - loss: 0.0349 - sparse_categorical_accuracy: 0.9960\n",
      "Epoch 182: saving model to training_1\\cp.ckpt\n",
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      "Epoch 182: saving model to training_1\\cp.ckpt\n",
      "682/932 [====================>.........] - ETA: 0s - loss: 0.0353 - sparse_categorical_accuracy: 0.9964\n",
      "Epoch 182: saving model to training_1\\cp.ckpt\n",
      "765/932 [=======================>......] - ETA: 0s - loss: 0.0355 - sparse_categorical_accuracy: 0.9964\n",
      "Epoch 182: saving model to training_1\\cp.ckpt\n",
      "849/932 [==========================>...] - ETA: 0s - loss: 0.0371 - sparse_categorical_accuracy: 0.9955\n",
      "Epoch 182: saving model to training_1\\cp.ckpt\n",
      "928/932 [============================>.] - ETA: 0s - loss: 0.0375 - sparse_categorical_accuracy: 0.9954\n",
      "Epoch 182: saving model to training_1\\cp.ckpt\n",
      "WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 4s 4ms/step - loss: 0.0374 - sparse_categorical_accuracy: 0.9954 - val_loss: 12.8312 - val_sparse_categorical_accuracy: 0.1973\n",
      "Epoch 183/10000\n",
      " 79/932 [=>............................] - ETA: 2s - loss: 0.0398 - sparse_categorical_accuracy: 0.9921\n",
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      "Epoch 183: saving model to training_1\\cp.ckpt\n",
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      "Epoch 183: saving model to training_1\\cp.ckpt\n",
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      "Epoch 183: saving model to training_1\\cp.ckpt\n",
      "469/932 [==============>...............] - ETA: 1s - loss: 0.0353 - sparse_categorical_accuracy: 0.9959\n",
      "Epoch 183: saving model to training_1\\cp.ckpt\n",
      "549/932 [================>.............] - ETA: 1s - loss: 0.0359 - sparse_categorical_accuracy: 0.9956\n",
      "Epoch 183: saving model to training_1\\cp.ckpt\n",
      "629/932 [===================>..........] - ETA: 1s - loss: 0.0359 - sparse_categorical_accuracy: 0.9955\n",
      "Epoch 183: saving model to training_1\\cp.ckpt\n",
      "710/932 [=====================>........] - ETA: 0s - loss: 0.0354 - sparse_categorical_accuracy: 0.9958\n",
      "Epoch 183: saving model to training_1\\cp.ckpt\n",
      "788/932 [========================>.....] - ETA: 0s - loss: 0.0357 - sparse_categorical_accuracy: 0.9958\n",
      "Epoch 183: saving model to training_1\\cp.ckpt\n",
      "869/932 [==========================>...] - ETA: 0s - loss: 0.0363 - sparse_categorical_accuracy: 0.9955\n",
      "Epoch 183: saving model to training_1\\cp.ckpt\n",
      "915/932 [============================>.] - ETA: 0s - loss: 0.0365 - sparse_categorical_accuracy: 0.9955WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 4s 4ms/step - loss: 0.0368 - sparse_categorical_accuracy: 0.9954 - val_loss: 12.8749 - val_sparse_categorical_accuracy: 0.1968\n",
      "Epoch 184/10000\n",
      " 17/932 [..............................] - ETA: 2s - loss: 0.0341 - sparse_categorical_accuracy: 0.9890\n",
      "Epoch 184: saving model to training_1\\cp.ckpt\n",
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      "Epoch 184: saving model to training_1\\cp.ckpt\n",
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      "Epoch 184: saving model to training_1\\cp.ckpt\n",
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      "344/932 [==========>...................] - ETA: 2s - loss: 0.0351 - sparse_categorical_accuracy: 0.9953\n",
      "Epoch 184: saving model to training_1\\cp.ckpt\n",
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      "Epoch 184: saving model to training_1\\cp.ckpt\n",
      "906/932 [============================>.] - ETA: 0s - loss: 0.0361 - sparse_categorical_accuracy: 0.9957\n",
      "Epoch 184: saving model to training_1\\cp.ckpt\n",
      "929/932 [============================>.] - ETA: 0s - loss: 0.0362 - sparse_categorical_accuracy: 0.9956WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 5s 5ms/step - loss: 0.0363 - sparse_categorical_accuracy: 0.9956 - val_loss: 12.9222 - val_sparse_categorical_accuracy: 0.1965\n",
      "Epoch 185/10000\n",
      " 53/932 [>.............................] - ETA: 4s - loss: 0.0296 - sparse_categorical_accuracy: 0.9988\n",
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      "Epoch 185: saving model to training_1\\cp.ckpt\n",
      "844/932 [==========================>...] - ETA: 0s - loss: 0.0350 - sparse_categorical_accuracy: 0.9959\n",
      "Epoch 185: saving model to training_1\\cp.ckpt\n",
      "931/932 [============================>.] - ETA: 0s - loss: 0.0358 - sparse_categorical_accuracy: 0.9955WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 5s 6ms/step - loss: 0.0358 - sparse_categorical_accuracy: 0.9955 - val_loss: 12.9661 - val_sparse_categorical_accuracy: 0.1962\n",
      "Epoch 186/10000\n",
      "  1/932 [..............................] - ETA: 5s - loss: 0.0364 - sparse_categorical_accuracy: 1.0000\n",
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      "795/932 [========================>.....] - ETA: 0s - loss: 0.0347 - sparse_categorical_accuracy: 0.9956\n",
      "Epoch 186: saving model to training_1\\cp.ckpt\n",
      "880/932 [===========================>..] - ETA: 0s - loss: 0.0352 - sparse_categorical_accuracy: 0.9954\n",
      "Epoch 186: saving model to training_1\\cp.ckpt\n",
      "926/932 [============================>.] - ETA: 0s - loss: 0.0354 - sparse_categorical_accuracy: 0.9954WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 4s 4ms/step - loss: 0.0354 - sparse_categorical_accuracy: 0.9954 - val_loss: 13.0139 - val_sparse_categorical_accuracy: 0.1968\n",
      "Epoch 187/10000\n",
      " 18/932 [..............................] - ETA: 2s - loss: 0.0244 - sparse_categorical_accuracy: 1.0000\n",
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      "Epoch 187: saving model to training_1\\cp.ckpt\n",
      "911/932 [============================>.] - ETA: 0s - loss: 0.0350 - sparse_categorical_accuracy: 0.9957\n",
      "Epoch 187: saving model to training_1\\cp.ckpt\n",
      "930/932 [============================>.] - ETA: 0s - loss: 0.0349 - sparse_categorical_accuracy: 0.9958WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 4s 4ms/step - loss: 0.0349 - sparse_categorical_accuracy: 0.9958 - val_loss: 13.0561 - val_sparse_categorical_accuracy: 0.1962\n",
      "Epoch 188/10000\n",
      " 44/932 [>.............................] - ETA: 3s - loss: 0.0304 - sparse_categorical_accuracy: 0.9972\n",
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      "691/932 [=====================>........] - ETA: 1s - loss: 0.0337 - sparse_categorical_accuracy: 0.9957\n",
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      "771/932 [=======================>......] - ETA: 0s - loss: 0.0340 - sparse_categorical_accuracy: 0.9955\n",
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      "Epoch 188: saving model to training_1\\cp.ckpt\n",
      "931/932 [============================>.] - ETA: 0s - loss: 0.0344 - sparse_categorical_accuracy: 0.9957WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 4s 5ms/step - loss: 0.0344 - sparse_categorical_accuracy: 0.9957 - val_loss: 13.1061 - val_sparse_categorical_accuracy: 0.1957\n",
      "Epoch 189/10000\n",
      "  1/932 [..............................] - ETA: 4s - loss: 0.0248 - sparse_categorical_accuracy: 1.0000\n",
      "Epoch 189: saving model to training_1\\cp.ckpt\n",
      " 83/932 [=>............................] - ETA: 3s - loss: 0.0299 - sparse_categorical_accuracy: 0.9977\n",
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      "Epoch 189: saving model to training_1\\cp.ckpt\n",
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      "Epoch 189: saving model to training_1\\cp.ckpt\n",
      "802/932 [========================>.....] - ETA: 0s - loss: 0.0328 - sparse_categorical_accuracy: 0.9956\n",
      "Epoch 189: saving model to training_1\\cp.ckpt\n",
      "884/932 [===========================>..] - ETA: 0s - loss: 0.0333 - sparse_categorical_accuracy: 0.9956\n",
      "Epoch 189: saving model to training_1\\cp.ckpt\n",
      "924/932 [============================>.] - ETA: 0s - loss: 0.0336 - sparse_categorical_accuracy: 0.9956WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 4s 5ms/step - loss: 0.0337 - sparse_categorical_accuracy: 0.9955 - val_loss: 13.1520 - val_sparse_categorical_accuracy: 0.1949\n",
      "Epoch 190/10000\n",
      " 30/932 [..............................] - ETA: 4s - loss: 0.0250 - sparse_categorical_accuracy: 0.9979\n",
      "Epoch 190: saving model to training_1\\cp.ckpt\n",
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      "Epoch 190: saving model to training_1\\cp.ckpt\n",
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      "Epoch 190: saving model to training_1\\cp.ckpt\n",
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      "434/932 [============>.................] - ETA: 2s - loss: 0.0310 - sparse_categorical_accuracy: 0.9967\n",
      "Epoch 190: saving model to training_1\\cp.ckpt\n",
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      "Epoch 190: saving model to training_1\\cp.ckpt\n",
      "591/932 [==================>...........] - ETA: 1s - loss: 0.0320 - sparse_categorical_accuracy: 0.9963\n",
      "Epoch 190: saving model to training_1\\cp.ckpt\n",
      "669/932 [====================>.........] - ETA: 1s - loss: 0.0321 - sparse_categorical_accuracy: 0.9963\n",
      "Epoch 190: saving model to training_1\\cp.ckpt\n",
      "749/932 [=======================>......] - ETA: 0s - loss: 0.0329 - sparse_categorical_accuracy: 0.9959\n",
      "Epoch 190: saving model to training_1\\cp.ckpt\n",
      "834/932 [=========================>....] - ETA: 0s - loss: 0.0332 - sparse_categorical_accuracy: 0.9957\n",
      "Epoch 190: saving model to training_1\\cp.ckpt\n",
      "912/932 [============================>.] - ETA: 0s - loss: 0.0330 - sparse_categorical_accuracy: 0.9960\n",
      "Epoch 190: saving model to training_1\\cp.ckpt\n",
      "918/932 [============================>.] - ETA: 0s - loss: 0.0330 - sparse_categorical_accuracy: 0.9960WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 4s 5ms/step - loss: 0.0333 - sparse_categorical_accuracy: 0.9958 - val_loss: 13.1991 - val_sparse_categorical_accuracy: 0.1968\n",
      "Epoch 191/10000\n",
      " 61/932 [>.............................] - ETA: 3s - loss: 0.0349 - sparse_categorical_accuracy: 0.9959\n",
      "Epoch 191: saving model to training_1\\cp.ckpt\n",
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      "Epoch 191: saving model to training_1\\cp.ckpt\n",
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      "Epoch 191: saving model to training_1\\cp.ckpt\n",
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      "Epoch 191: saving model to training_1\\cp.ckpt\n",
      "459/932 [=============>................] - ETA: 1s - loss: 0.0311 - sparse_categorical_accuracy: 0.9970\n",
      "Epoch 191: saving model to training_1\\cp.ckpt\n",
      "543/932 [================>.............] - ETA: 1s - loss: 0.0317 - sparse_categorical_accuracy: 0.9967\n",
      "Epoch 191: saving model to training_1\\cp.ckpt\n",
      "617/932 [==================>...........] - ETA: 1s - loss: 0.0312 - sparse_categorical_accuracy: 0.9969\n",
      "Epoch 191: saving model to training_1\\cp.ckpt\n",
      "691/932 [=====================>........] - ETA: 0s - loss: 0.0321 - sparse_categorical_accuracy: 0.9963\n",
      "Epoch 191: saving model to training_1\\cp.ckpt\n",
      "782/932 [========================>.....] - ETA: 0s - loss: 0.0323 - sparse_categorical_accuracy: 0.9962\n",
      "Epoch 191: saving model to training_1\\cp.ckpt\n",
      "859/932 [==========================>...] - ETA: 0s - loss: 0.0326 - sparse_categorical_accuracy: 0.9961\n",
      "Epoch 191: saving model to training_1\\cp.ckpt\n",
      "923/932 [============================>.] - ETA: 0s - loss: 0.0329 - sparse_categorical_accuracy: 0.9959WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 4s 5ms/step - loss: 0.0329 - sparse_categorical_accuracy: 0.9958 - val_loss: 13.2454 - val_sparse_categorical_accuracy: 0.1962\n",
      "Epoch 192/10000\n",
      "  1/932 [..............................] - ETA: 5s - loss: 0.0255 - sparse_categorical_accuracy: 1.0000\n",
      "Epoch 192: saving model to training_1\\cp.ckpt\n",
      " 89/932 [=>............................] - ETA: 3s - loss: 0.0287 - sparse_categorical_accuracy: 0.9979\n",
      "Epoch 192: saving model to training_1\\cp.ckpt\n",
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      "Epoch 192: saving model to training_1\\cp.ckpt\n",
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      "Epoch 192: saving model to training_1\\cp.ckpt\n",
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      "Epoch 192: saving model to training_1\\cp.ckpt\n",
      "406/932 [============>.................] - ETA: 2s - loss: 0.0302 - sparse_categorical_accuracy: 0.9968\n",
      "Epoch 192: saving model to training_1\\cp.ckpt\n",
      "486/932 [==============>...............] - ETA: 1s - loss: 0.0299 - sparse_categorical_accuracy: 0.9970\n",
      "Epoch 192: saving model to training_1\\cp.ckpt\n",
      "567/932 [=================>............] - ETA: 1s - loss: 0.0310 - sparse_categorical_accuracy: 0.9966\n",
      "Epoch 192: saving model to training_1\\cp.ckpt\n",
      "646/932 [===================>..........] - ETA: 1s - loss: 0.0311 - sparse_categorical_accuracy: 0.9966\n",
      "Epoch 192: saving model to training_1\\cp.ckpt\n",
      "725/932 [======================>.......] - ETA: 0s - loss: 0.0317 - sparse_categorical_accuracy: 0.9962\n",
      "Epoch 192: saving model to training_1\\cp.ckpt\n",
      "807/932 [========================>.....] - ETA: 0s - loss: 0.0322 - sparse_categorical_accuracy: 0.9960\n",
      "Epoch 192: saving model to training_1\\cp.ckpt\n",
      "882/932 [===========================>..] - ETA: 0s - loss: 0.0322 - sparse_categorical_accuracy: 0.9961\n",
      "Epoch 192: saving model to training_1\\cp.ckpt\n",
      "922/932 [============================>.] - ETA: 0s - loss: 0.0325 - sparse_categorical_accuracy: 0.9959WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 4s 4ms/step - loss: 0.0325 - sparse_categorical_accuracy: 0.9959 - val_loss: 13.2896 - val_sparse_categorical_accuracy: 0.1965\n",
      "Epoch 193/10000\n",
      " 35/932 [>.............................] - ETA: 4s - loss: 0.0297 - sparse_categorical_accuracy: 0.9964\n",
      "Epoch 193: saving model to training_1\\cp.ckpt\n",
      "108/932 [==>...........................] - ETA: 3s - loss: 0.0284 - sparse_categorical_accuracy: 0.9965\n",
      "Epoch 193: saving model to training_1\\cp.ckpt\n",
      "191/932 [=====>........................] - ETA: 3s - loss: 0.0292 - sparse_categorical_accuracy: 0.9964\n",
      "Epoch 193: saving model to training_1\\cp.ckpt\n",
      "279/932 [=======>......................] - ETA: 2s - loss: 0.0287 - sparse_categorical_accuracy: 0.9971\n",
      "Epoch 193: saving model to training_1\\cp.ckpt\n",
      "357/932 [==========>...................] - ETA: 2s - loss: 0.0291 - sparse_categorical_accuracy: 0.9972\n",
      "Epoch 193: saving model to training_1\\cp.ckpt\n",
      "437/932 [=============>................] - ETA: 2s - loss: 0.0295 - sparse_categorical_accuracy: 0.9973\n",
      "Epoch 193: saving model to training_1\\cp.ckpt\n",
      "515/932 [===============>..............] - ETA: 1s - loss: 0.0300 - sparse_categorical_accuracy: 0.9970\n",
      "Epoch 193: saving model to training_1\\cp.ckpt\n",
      "597/932 [==================>...........] - ETA: 1s - loss: 0.0309 - sparse_categorical_accuracy: 0.9961\n",
      "Epoch 193: saving model to training_1\\cp.ckpt\n",
      "679/932 [====================>.........] - ETA: 1s - loss: 0.0312 - sparse_categorical_accuracy: 0.9960\n",
      "Epoch 193: saving model to training_1\\cp.ckpt\n",
      "756/932 [=======================>......] - ETA: 0s - loss: 0.0311 - sparse_categorical_accuracy: 0.9960\n",
      "Epoch 193: saving model to training_1\\cp.ckpt\n",
      "830/932 [=========================>....] - ETA: 0s - loss: 0.0316 - sparse_categorical_accuracy: 0.9959\n",
      "Epoch 193: saving model to training_1\\cp.ckpt\n",
      "918/932 [============================>.] - ETA: 0s - loss: 0.0318 - sparse_categorical_accuracy: 0.9958\n",
      "Epoch 193: saving model to training_1\\cp.ckpt\n",
      "932/932 [==============================] - ETA: 0s - loss: 0.0317 - sparse_categorical_accuracy: 0.9959WARNING:tensorflow:Early stopping conditioned on metric `val_accuracy` which is not available. Available metrics are: loss,sparse_categorical_accuracy,val_loss,val_sparse_categorical_accuracy\n",
      "932/932 [==============================] - 5s 5ms/step - loss: 0.0317 - sparse_categorical_accuracy: 0.9959 - val_loss: 13.3453 - val_sparse_categorical_accuracy: 0.1952\n",
      "Epoch 194/10000\n",
      " 58/932 [>.............................] - ETA: 3s - loss: 0.0333 - sparse_categorical_accuracy: 0.9935\n",
      "Epoch 194: saving model to training_1\\cp.ckpt\n",
      "147/932 [===>..........................] - ETA: 3s - loss: 0.0302 - sparse_categorical_accuracy: 0.9953\n",
      "Epoch 194: saving model to training_1\\cp.ckpt\n",
      "218/932 [======>.......................] - ETA: 3s - loss: 0.0300 - sparse_categorical_accuracy: 0.9954\n",
      "Epoch 194: saving model to training_1\\cp.ckpt\n",
      "301/932 [========>.....................] - ETA: 3s - loss: 0.0300 - sparse_categorical_accuracy: 0.9961\n",
      "Epoch 194: saving model to training_1\\cp.ckpt\n",
      "379/932 [===========>..................] - ETA: 2s - loss: 0.0309 - sparse_categorical_accuracy: 0.9954\n",
      "Epoch 194: saving model to training_1\\cp.ckpt\n",
      "465/932 [=============>................] - ETA: 2s - loss: 0.0309 - sparse_categorical_accuracy: 0.9956\n",
      "Epoch 194: saving model to training_1\\cp.ckpt\n",
      "481/932 [==============>...............] - ETA: 2s - loss: 0.0312 - sparse_categorical_accuracy: 0.9955"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mKeyboardInterrupt\u001B[0m                         Traceback (most recent call last)",
      "Input \u001B[1;32mIn [38]\u001B[0m, in \u001B[0;36m<cell line: 1>\u001B[1;34m()\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[43mmodel\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfit\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtrain_data\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtrain_label\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mepochs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m10000\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[0;32m      2\u001B[0m \u001B[43m          \u001B[49m\u001B[43mvalidation_data\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43mtest_data\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtest_label\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m      3\u001B[0m \u001B[43m          \u001B[49m\u001B[43mcallbacks\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m[\u001B[49m\u001B[43mcp_callback\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mearlystop_callback\u001B[49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m      4\u001B[0m \u001B[43m          \u001B[49m\u001B[43mbatch_size\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mbatch_size\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m      5\u001B[0m \u001B[43m          \u001B[49m\u001B[43mverbose\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m1\u001B[39;49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32mD:\\EnglishStandardPath\\DevProgramsFile\\Python\\python39\\lib\\site-packages\\keras\\utils\\traceback_utils.py:64\u001B[0m, in \u001B[0;36mfilter_traceback.<locals>.error_handler\u001B[1;34m(*args, **kwargs)\u001B[0m\n\u001B[0;32m     62\u001B[0m filtered_tb \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[0;32m     63\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m---> 64\u001B[0m   \u001B[38;5;28;01mreturn\u001B[39;00m fn(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[0;32m     65\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mException\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m e:  \u001B[38;5;66;03m# pylint: disable=broad-except\u001B[39;00m\n\u001B[0;32m     66\u001B[0m   filtered_tb \u001B[38;5;241m=\u001B[39m _process_traceback_frames(e\u001B[38;5;241m.\u001B[39m__traceback__)\n",
      "File \u001B[1;32mD:\\EnglishStandardPath\\DevProgramsFile\\Python\\python39\\lib\\site-packages\\keras\\engine\\training.py:1409\u001B[0m, in \u001B[0;36mModel.fit\u001B[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001B[0m\n\u001B[0;32m   1402\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m tf\u001B[38;5;241m.\u001B[39mprofiler\u001B[38;5;241m.\u001B[39mexperimental\u001B[38;5;241m.\u001B[39mTrace(\n\u001B[0;32m   1403\u001B[0m     \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mtrain\u001B[39m\u001B[38;5;124m'\u001B[39m,\n\u001B[0;32m   1404\u001B[0m     epoch_num\u001B[38;5;241m=\u001B[39mepoch,\n\u001B[0;32m   1405\u001B[0m     step_num\u001B[38;5;241m=\u001B[39mstep,\n\u001B[0;32m   1406\u001B[0m     batch_size\u001B[38;5;241m=\u001B[39mbatch_size,\n\u001B[0;32m   1407\u001B[0m     _r\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m1\u001B[39m):\n\u001B[0;32m   1408\u001B[0m   callbacks\u001B[38;5;241m.\u001B[39mon_train_batch_begin(step)\n\u001B[1;32m-> 1409\u001B[0m   tmp_logs \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mtrain_function\u001B[49m\u001B[43m(\u001B[49m\u001B[43miterator\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m   1410\u001B[0m   \u001B[38;5;28;01mif\u001B[39;00m data_handler\u001B[38;5;241m.\u001B[39mshould_sync:\n\u001B[0;32m   1411\u001B[0m     context\u001B[38;5;241m.\u001B[39masync_wait()\n",
      "File \u001B[1;32mD:\\EnglishStandardPath\\DevProgramsFile\\Python\\python39\\lib\\site-packages\\tensorflow\\python\\util\\traceback_utils.py:150\u001B[0m, in \u001B[0;36mfilter_traceback.<locals>.error_handler\u001B[1;34m(*args, **kwargs)\u001B[0m\n\u001B[0;32m    148\u001B[0m filtered_tb \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[0;32m    149\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m--> 150\u001B[0m   \u001B[38;5;28;01mreturn\u001B[39;00m fn(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[0;32m    151\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mException\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[0;32m    152\u001B[0m   filtered_tb \u001B[38;5;241m=\u001B[39m _process_traceback_frames(e\u001B[38;5;241m.\u001B[39m__traceback__)\n",
      "File \u001B[1;32mD:\\EnglishStandardPath\\DevProgramsFile\\Python\\python39\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py:915\u001B[0m, in \u001B[0;36mFunction.__call__\u001B[1;34m(self, *args, **kwds)\u001B[0m\n\u001B[0;32m    912\u001B[0m compiler \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mxla\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_jit_compile \u001B[38;5;28;01melse\u001B[39;00m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mnonXla\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m    914\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m OptionalXlaContext(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_jit_compile):\n\u001B[1;32m--> 915\u001B[0m   result \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_call(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwds)\n\u001B[0;32m    917\u001B[0m new_tracing_count \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mexperimental_get_tracing_count()\n\u001B[0;32m    918\u001B[0m without_tracing \u001B[38;5;241m=\u001B[39m (tracing_count \u001B[38;5;241m==\u001B[39m new_tracing_count)\n",
      "File \u001B[1;32mD:\\EnglishStandardPath\\DevProgramsFile\\Python\\python39\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py:947\u001B[0m, in \u001B[0;36mFunction._call\u001B[1;34m(self, *args, **kwds)\u001B[0m\n\u001B[0;32m    944\u001B[0m   \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_lock\u001B[38;5;241m.\u001B[39mrelease()\n\u001B[0;32m    945\u001B[0m   \u001B[38;5;66;03m# In this case we have created variables on the first call, so we run the\u001B[39;00m\n\u001B[0;32m    946\u001B[0m   \u001B[38;5;66;03m# defunned version which is guaranteed to never create variables.\u001B[39;00m\n\u001B[1;32m--> 947\u001B[0m   \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_stateless_fn(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwds)  \u001B[38;5;66;03m# pylint: disable=not-callable\u001B[39;00m\n\u001B[0;32m    948\u001B[0m \u001B[38;5;28;01melif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_stateful_fn \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m    949\u001B[0m   \u001B[38;5;66;03m# Release the lock early so that multiple threads can perform the call\u001B[39;00m\n\u001B[0;32m    950\u001B[0m   \u001B[38;5;66;03m# in parallel.\u001B[39;00m\n\u001B[0;32m    951\u001B[0m   \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_lock\u001B[38;5;241m.\u001B[39mrelease()\n",
      "File \u001B[1;32mD:\\EnglishStandardPath\\DevProgramsFile\\Python\\python39\\lib\\site-packages\\tensorflow\\python\\eager\\function.py:2453\u001B[0m, in \u001B[0;36mFunction.__call__\u001B[1;34m(self, *args, **kwargs)\u001B[0m\n\u001B[0;32m   2450\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_lock:\n\u001B[0;32m   2451\u001B[0m   (graph_function,\n\u001B[0;32m   2452\u001B[0m    filtered_flat_args) \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_maybe_define_function(args, kwargs)\n\u001B[1;32m-> 2453\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mgraph_function\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_call_flat\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m   2454\u001B[0m \u001B[43m    \u001B[49m\u001B[43mfiltered_flat_args\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcaptured_inputs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mgraph_function\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcaptured_inputs\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32mD:\\EnglishStandardPath\\DevProgramsFile\\Python\\python39\\lib\\site-packages\\tensorflow\\python\\eager\\function.py:1860\u001B[0m, in \u001B[0;36mConcreteFunction._call_flat\u001B[1;34m(self, args, captured_inputs, cancellation_manager)\u001B[0m\n\u001B[0;32m   1856\u001B[0m possible_gradient_type \u001B[38;5;241m=\u001B[39m gradients_util\u001B[38;5;241m.\u001B[39mPossibleTapeGradientTypes(args)\n\u001B[0;32m   1857\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m (possible_gradient_type \u001B[38;5;241m==\u001B[39m gradients_util\u001B[38;5;241m.\u001B[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001B[0;32m   1858\u001B[0m     \u001B[38;5;129;01mand\u001B[39;00m executing_eagerly):\n\u001B[0;32m   1859\u001B[0m   \u001B[38;5;66;03m# No tape is watching; skip to running the function.\u001B[39;00m\n\u001B[1;32m-> 1860\u001B[0m   \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_build_call_outputs(\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_inference_function\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcall\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m   1861\u001B[0m \u001B[43m      \u001B[49m\u001B[43mctx\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcancellation_manager\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mcancellation_manager\u001B[49m\u001B[43m)\u001B[49m)\n\u001B[0;32m   1862\u001B[0m forward_backward \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_select_forward_and_backward_functions(\n\u001B[0;32m   1863\u001B[0m     args,\n\u001B[0;32m   1864\u001B[0m     possible_gradient_type,\n\u001B[0;32m   1865\u001B[0m     executing_eagerly)\n\u001B[0;32m   1866\u001B[0m forward_function, args_with_tangents \u001B[38;5;241m=\u001B[39m forward_backward\u001B[38;5;241m.\u001B[39mforward()\n",
      "File \u001B[1;32mD:\\EnglishStandardPath\\DevProgramsFile\\Python\\python39\\lib\\site-packages\\tensorflow\\python\\eager\\function.py:497\u001B[0m, in \u001B[0;36m_EagerDefinedFunction.call\u001B[1;34m(self, ctx, args, cancellation_manager)\u001B[0m\n\u001B[0;32m    495\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m _InterpolateFunctionError(\u001B[38;5;28mself\u001B[39m):\n\u001B[0;32m    496\u001B[0m   \u001B[38;5;28;01mif\u001B[39;00m cancellation_manager \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[1;32m--> 497\u001B[0m     outputs \u001B[38;5;241m=\u001B[39m \u001B[43mexecute\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mexecute\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m    498\u001B[0m \u001B[43m        \u001B[49m\u001B[38;5;28;43mstr\u001B[39;49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msignature\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mname\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    499\u001B[0m \u001B[43m        \u001B[49m\u001B[43mnum_outputs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_num_outputs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    500\u001B[0m \u001B[43m        \u001B[49m\u001B[43minputs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    501\u001B[0m \u001B[43m        \u001B[49m\u001B[43mattrs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mattrs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    502\u001B[0m \u001B[43m        \u001B[49m\u001B[43mctx\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mctx\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    503\u001B[0m   \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m    504\u001B[0m     outputs \u001B[38;5;241m=\u001B[39m execute\u001B[38;5;241m.\u001B[39mexecute_with_cancellation(\n\u001B[0;32m    505\u001B[0m         \u001B[38;5;28mstr\u001B[39m(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39msignature\u001B[38;5;241m.\u001B[39mname),\n\u001B[0;32m    506\u001B[0m         num_outputs\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_num_outputs,\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m    509\u001B[0m         ctx\u001B[38;5;241m=\u001B[39mctx,\n\u001B[0;32m    510\u001B[0m         cancellation_manager\u001B[38;5;241m=\u001B[39mcancellation_manager)\n",
      "File \u001B[1;32mD:\\EnglishStandardPath\\DevProgramsFile\\Python\\python39\\lib\\site-packages\\tensorflow\\python\\eager\\execute.py:54\u001B[0m, in \u001B[0;36mquick_execute\u001B[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001B[0m\n\u001B[0;32m     52\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m     53\u001B[0m   ctx\u001B[38;5;241m.\u001B[39mensure_initialized()\n\u001B[1;32m---> 54\u001B[0m   tensors \u001B[38;5;241m=\u001B[39m \u001B[43mpywrap_tfe\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mTFE_Py_Execute\u001B[49m\u001B[43m(\u001B[49m\u001B[43mctx\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_handle\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdevice_name\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mop_name\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m     55\u001B[0m \u001B[43m                                      \u001B[49m\u001B[43minputs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mattrs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mnum_outputs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m     56\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m core\u001B[38;5;241m.\u001B[39m_NotOkStatusException \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[0;32m     57\u001B[0m   \u001B[38;5;28;01mif\u001B[39;00m name \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n",
      "\u001B[1;31mKeyboardInterrupt\u001B[0m: "
     ]
    }
   ],
   "source": [
    "model.fit(train_data, train_label, epochs=10000,\n",
    "          validation_data=(test_data, test_label),\n",
    "          callbacks=[cp_callback, earlystop_callback],\n",
    "          batch_size=batch_size,\n",
    "          verbose=1)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "117/117 - 0s - loss: 13.3616 - sparse_categorical_accuracy: 0.1952 - 210ms/epoch - 2ms/step\n",
      "[13.361594200134277, 0.19516777992248535]\n"
     ]
    }
   ],
   "source": [
    "results = model.evaluate(test_data, test_label, verbose=2)\n",
    "print(results)  # 好垃圾，没有加隐藏层，对测试集反而好呢"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "outputs": [
    {
     "data": {
      "text/plain": "dict_keys(['loss', 'accuracy'])"
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history_dict = history.history\n",
    "history_dict.keys()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "acc = history_dict['accuracy']\n",
    "loss = history_dict['loss']\n",
    "\n",
    "epochs = range(1, len(acc) + 1)\n",
    "\n",
    "# “bo”代表 \"蓝点\"\n",
    "plt.plot(epochs, loss, 'b', label='Training loss')\n",
    "# b代表“蓝色实线”\n",
    "plt.title('Training and validation loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ],
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  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Exception encountered when calling layer \"sequential_10\" (type Sequential).\n\nInput 0 of layer \"global_average_pooling1d_10\" is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (56, 16)\n\nCall arguments received by layer \"sequential_10\" (type Sequential):\n  • inputs=tf.Tensor(shape=(56,), dtype=int32)\n  • training=None\n  • mask=None",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mValueError\u001B[0m                                Traceback (most recent call last)",
      "Input \u001B[1;32mIn [105]\u001B[0m, in \u001B[0;36m<cell line: 1>\u001B[1;34m()\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[43mmodel\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtrain_data\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;241;43m0\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32mD:\\EnglishStandardPath\\DevProgramsFile\\Python\\python39\\lib\\site-packages\\keras\\utils\\traceback_utils.py:67\u001B[0m, in \u001B[0;36mfilter_traceback.<locals>.error_handler\u001B[1;34m(*args, **kwargs)\u001B[0m\n\u001B[0;32m     65\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mException\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m e:  \u001B[38;5;66;03m# pylint: disable=broad-except\u001B[39;00m\n\u001B[0;32m     66\u001B[0m   filtered_tb \u001B[38;5;241m=\u001B[39m _process_traceback_frames(e\u001B[38;5;241m.\u001B[39m__traceback__)\n\u001B[1;32m---> 67\u001B[0m   \u001B[38;5;28;01mraise\u001B[39;00m e\u001B[38;5;241m.\u001B[39mwith_traceback(filtered_tb) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;28mNone\u001B[39m\n\u001B[0;32m     68\u001B[0m \u001B[38;5;28;01mfinally\u001B[39;00m:\n\u001B[0;32m     69\u001B[0m   \u001B[38;5;28;01mdel\u001B[39;00m filtered_tb\n",
      "File \u001B[1;32mD:\\EnglishStandardPath\\DevProgramsFile\\Python\\python39\\lib\\site-packages\\keras\\engine\\input_spec.py:214\u001B[0m, in \u001B[0;36massert_input_compatibility\u001B[1;34m(input_spec, inputs, layer_name)\u001B[0m\n\u001B[0;32m    212\u001B[0m   ndim \u001B[38;5;241m=\u001B[39m shape\u001B[38;5;241m.\u001B[39mrank\n\u001B[0;32m    213\u001B[0m   \u001B[38;5;28;01mif\u001B[39;00m ndim \u001B[38;5;241m!=\u001B[39m spec\u001B[38;5;241m.\u001B[39mndim:\n\u001B[1;32m--> 214\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mInput \u001B[39m\u001B[38;5;132;01m{\u001B[39;00minput_index\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m of layer \u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mlayer_name\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m \u001B[39m\u001B[38;5;124m'\u001B[39m\n\u001B[0;32m    215\u001B[0m                      \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mis incompatible with the layer: \u001B[39m\u001B[38;5;124m'\u001B[39m\n\u001B[0;32m    216\u001B[0m                      \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mexpected ndim=\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mspec\u001B[38;5;241m.\u001B[39mndim\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m, found ndim=\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mndim\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m. \u001B[39m\u001B[38;5;124m'\u001B[39m\n\u001B[0;32m    217\u001B[0m                      \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mFull shape received: \u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[38;5;28mtuple\u001B[39m(shape)\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m'\u001B[39m)\n\u001B[0;32m    218\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m spec\u001B[38;5;241m.\u001B[39mmax_ndim \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m    219\u001B[0m   ndim \u001B[38;5;241m=\u001B[39m x\u001B[38;5;241m.\u001B[39mshape\u001B[38;5;241m.\u001B[39mrank\n",
      "\u001B[1;31mValueError\u001B[0m: Exception encountered when calling layer \"sequential_10\" (type Sequential).\n\nInput 0 of layer \"global_average_pooling1d_10\" is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (56, 16)\n\nCall arguments received by layer \"sequential_10\" (type Sequential):\n  • inputs=tf.Tensor(shape=(56,), dtype=int32)\n  • training=None\n  • mask=None"
     ]
    }
   ],
   "source": [
    "# model(train_data[0])"
   ],
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    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
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